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39 produkter

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39 produkter

Privacy-Aware Knowledge Discovery
Novel Applications and New Techniques
AvElena Ferrari,Francesco Bonchi
E-bok
Engelska, 2010
1 204 kr
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Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities
Privacy-Aware Knowledge Discovery
Novel Applications and New Techniques
AvFrancesco Bonchi,Elena Ferrari
Häftad, Engelska, 2019
1 019 kr
Skickas inom 10-15 vardagar
Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results—they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques that can be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility data analysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives.While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation.
Privacy-Aware Knowledge Discovery
Novel Applications and New Techniques
AvFrancesco Bonchi,Elena Ferrari
Inbunden, Engelska, 2010
1 840 kr
Skickas inom 10-15 vardagar
Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results—they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques that can be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility data analysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives.While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation.
Del 11051 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I
AvMichele Berlingerio,Francesco Bonchim. fl.
Häftad, Engelska, 2019
576 kr
Skickas inom 10-15 vardagar
The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows:Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation.Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I
AvGeorgiana Ifrim,Neil Hurleym. fl.
E-bok
Engelska, 2019
739 kr
Läs direkt efter köp
The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018.
The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track.
The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
Del 11052 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II
AvMichele Berlingerio,Francesco Bonchim. fl.
Häftad, Engelska, 2019
1 144 kr
Skickas inom 10-15 vardagar
The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows:Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation.Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II
AvGeorgiana Ifrim,Neil Hurleym. fl.
E-bok
Engelska, 2019
1 455 kr
Läs direkt efter köp
The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018.
The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track.
The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
Del 14169 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part I
AvDanai Koutra,Claudia Plantm. fl.
Häftad, Engelska, 2023
1 234 kr
Skickas inom 5-8 vardagar
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part I
AvFrancesco Bonchi,Elena Baralism. fl.
E-bok
Engelska, 2023
1 617 kr
Läs direkt efter köp
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.
Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.
Part V: Robustness; Time Series; Transfer and Multitask Learning.
Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Del 14170 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II
AvDanai Koutra,Claudia Plantm. fl.
Häftad, Engelska, 2023
1 258 kr
Skickas inom 10-15 vardagar
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II
AvFrancesco Bonchi,Elena Baralism. fl.
E-bok
Engelska, 2023
1 617 kr
Läs direkt efter köp
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.
Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.
Part V: Robustness; Time Series; Transfer and Multitask Learning.
Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Del 14171 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III
AvDanai Koutra,Claudia Plantm. fl.
Häftad, Engelska, 2023
1 121 kr
Skickas inom 10-15 vardagar
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.Chapter 41 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III
AvFrancesco Bonchi,Elena Baralism. fl.
E-bok
Engelska, 2023
1 377 kr
Läs direkt efter köp
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.
Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.
Part V: Robustness; Time Series; Transfer and Multitask Learning.
Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.Chapter 41 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.
Del 14172 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV
AvDanai Koutra,Claudia Plantm. fl.
Häftad, Engelska, 2023
1 234 kr
Skickas inom 5-8 vardagar
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV
AvFrancesco Bonchi,Elena Baralism. fl.
E-bok
Engelska, 2023
1 634 kr
Läs direkt efter köp
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.
Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.
Part V: Robustness; Time Series; Transfer and Multitask Learning.
Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Del 14173 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V
AvDanai Koutra,Claudia Plantm. fl.
Häftad, Engelska, 2023
788 kr
Skickas inom 5-8 vardagar
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V
AvFrancesco Bonchi,Elena Baralism. fl.
E-bok
Engelska, 2023
975 kr
Läs direkt efter köp
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.
Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.
Part V: Robustness; Time Series; Transfer and Multitask Learning.
Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Del 14174 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VI
AvGianmarco De Francisci Morales,Claudia Perlichm. fl.
Häftad, Engelska, 2023
973 kr
Skickas inom 10-15 vardagar
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VI
AvFrancesco Bonchi,Elena Baralism. fl.
E-bok
Engelska, 2023
1 211 kr
Läs direkt efter köp
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.
Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.
Part V: Robustness; Time Series; Transfer and Multitask Learning.
Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Del 14175 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII
AvGianmarco De Francisci Morales,Claudia Perlichm. fl.
Häftad, Engelska, 2023
746 kr
Skickas inom 10-15 vardagar
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII
AvFrancesco Bonchi,Elena Baralism. fl.
E-bok
Engelska, 2023
894 kr
Läs direkt efter köp
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.
Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.
Part V: Robustness; Time Series; Transfer and Multitask Learning.
Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Correlation Clustering
AvFrancesco Bonchi,David García-Sorianom. fl.
Häftad, Engelska, 2022
669 kr
Skickas inom 10-15 vardagar
Given a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of clusters to maximize the similarity of the objects within the same cluster and minimize the similarity of the objects in different clusters. In most of the variants of correlation clustering, the number of clusters is not a given parameter; instead, the optimal number of clusters is automatically determined. Correlation clustering is perhaps the most natural formulation of clustering: as it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and, particularly, makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain. Despite its simplicity, generality, and wide applicability, correlation clustering has so far received much more attention from an algorithmic-theory perspective than from the data-mining community. The goal of this lecture is to show how correlation clustering can be a powerful addition to the toolkit of a data-mining researcher and practitioner, and to encourage further research in the area.
Correlation Clustering
AvFrancesco Gullo,David Garcia-Sorianom. fl.
E-bok
PDF, Engelska, 2022
868 kr
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Given a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of clusters to maximize the similarity of the objects within the same cluster and minimize the similarity of the objects in different clusters. In most of the variants of correlation clustering, the number of clusters is not a given parameter; instead, the optimal number of clusters is automatically determined. Correlation clustering is perhaps the most natural formulation of clustering: as it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and, particularly, makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain. Despite its simplicity, generality, and wide applicability, correlation clustering has so far received much more attention from an algorithmic-theory perspective than from the data-mining community. The goal of this lecture is to show how correlation clustering can be a powerful addition to the toolkit of a data-mining researcher and practitioner, and to encourage further research in the area.
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, Demo Track and Industrial Track
European Conference, ECML PKDD 2026, Naples, Italy, September 7–11, 2026, Proceedings, Part X
AvMitra Baratchi,Francesco Bonchim. fl.
Häftad, Engelska, 2026
960 kr
Kommande
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
Del 9286 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III
AvAlbert Bifet,Michael Maym. fl.
Häftad, Engelska, 2015
566 kr
Skickas inom 10-15 vardagar
The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III
AvMyra Spiliopoulou,Jaime Cardosom. fl.
E-bok
PDF, Engelska, 2015
732 kr
Läs direkt efter köp
The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Knowledge Discovery in Inductive Databases
4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers
AvFrancesco Bonchi,Jean-Francois Boulicaut
Häftad, Engelska, 2006
566 kr
Skickas inom 10-15 vardagar
The4thInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (KDID 2005) was held in Porto, Portugal, on October 3, 2005 in conjunction with the 16th European Conference on Machine Learning and the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Ever since the start of the ?eld of data mining, it has been realized that the integration of the database technology into knowledge discovery processes was a crucial issue. This vision has been formalized into the inductive database perspective introduced by T. Imielinski and H. Mannila (CACM 1996, 39(11)). The main idea is to consider knowledge discovery as an extended querying p- cess for which relevant query languages are to be speci?ed. Therefore, inductive databases might contain not only the usual data but also inductive gener- izations (e. g. , patterns, models) holding within the data. Despite many recent developments, there is still a pressing need to understand the central issues in inductive databases. Constraint-based mining has been identi?ed as a core technology for inductive querying, and promising results have been obtained for rather simple types of patterns (e. g. , itemsets, sequential patterns). However, constraint-based mining of models remains a quite open issue. Also, coupling schemes between the available database technology and inductive querying p- posals are not yet well understood. Finally, the de?nition of a general purpose inductive query language is still an on-going quest.
Knowledge Discovery in Inductive Databases
4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers
AvJean-Francois Boulicaut,Francesco Bonchi
E-bok
PDF, Engelska, 2006
739 kr
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The4thInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (KDID 2005) was held in Porto, Portugal, on October 3, 2005 in conjunction with the 16th European Conference on Machine Learning and the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Ever since the start of the ?eld of data mining, it has been realized that the integration of the database technology into knowledge discovery processes was a crucial issue. This vision has been formalized into the inductive database perspective introduced by T. Imielinski and H. Mannila (CACM 1996, 39(11)). The main idea is to consider knowledge discovery as an extended querying p- cess for which relevant query languages are to be speci?ed. Therefore, inductive databases might contain not only the usual data but also inductive gener- izations (e. g. , patterns, models) holding within the data. Despite many recent developments, there is still a pressing need to understand the central issues in inductive databases. Constraint-based mining has been identi?ed as a core technology for inductive querying, and promising results have been obtained for rather simple types of patterns (e. g. , itemsets, sequential patterns). However, constraint-based mining of models remains a quite open issue. Also, coupling schemes between the available database technology and inductive querying p- posals are not yet well understood. Finally, the de?nition of a general purpose inductive query language is still an on-going quest.
Privacy, Security, and Trust in KDD
First ACM SIGKDD International Workshop, PinKDD 2007, San Jose, CA, USA, August 12, 2007, Revised, Selected Papers
AvFrancesco Bonchi,Elena Ferrarim. fl.
Häftad, Engelska, 2008
566 kr
Skickas inom 10-15 vardagar
Vast amounts of data are collected by service providers and system administ- tors, and are available in public information systems. Data mining technologies provide an ideal framework to assist in analyzing such collections for computer security and surveillance-related endeavors. For instance, system administrators can apply data mining to summarize activity patterns in access logs so that potential malicious incidents can be further investigated. Beyond computer - curity, data mining technology supports intelligence gathering and summari- tion for homeland security. For years, and most recently fueled by events such as September 11, 2001, government agencies have focused on developing and applying data mining technologies to monitor terrorist behaviors in public and private data collections. Theapplicationof data mining to person-speci?cdata raisesseriousconcerns regarding data con?dentiality and citizens' privacy rights. These concerns have led to the adoption of various legislation and policy controls.In 2005, the - ropean Union passed a data-retention directive that requires all telephone and Internetservice providersto store data ontheir consumers for up to two yearsto assist in the prevention of terrorismand organized crime. Similar data-retention regulationproposalsareunderheateddebateintheUnitedStatesCongress. Yet, the debate often focuses on ethical or policy aspects of the problem, such that resolutions have polarized consequences; e. g. , an organization can either share data for data mining purposes or it can not. Fortunately, computer scientists, and data mining researchers in particular, have recognized that technology can beconstructedtosupportlesspolarizedsolutions. Computerscientistsaredev- oping technologies that enable data mining goals without sacri?cing the privacy and security of the individuals to whom the data correspond.
Privacy, Security, and Trust in KDD
First ACM SIGKDD International Workshop, PinKDD 2007, San Jose, CA, USA, August 12, 2007, Revised, Selected Papers
AvYucel Saygin,Bradley Malinm. fl.
E-bok
PDF, Engelska, 2008
732 kr
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Francesco Bonchi – författare

Privacy-Aware Knowledge Discovery

Privacy-Aware Knowledge Discovery

Privacy-Aware Knowledge Discovery

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track

Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track

Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track

Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track

Correlation Clustering

Correlation Clustering

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, Demo Track and Industrial Track

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases

Knowledge Discovery in Inductive Databases

Knowledge Discovery in Inductive Databases

Privacy, Security, and Trust in KDD

Privacy, Security, and Trust in KDD