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28 produkter
28 produkter
Inbunden, Engelska, 2010
1 119 kr
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This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.
E-bok
PDF, Engelska, 20101 416 kr
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This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.
Inbunden, Engelska, 2025
1 413 kr
Kommande
This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques.The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics.The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.
Häftad, Engelska, 2014
1 119 kr
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This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.
Del 11828 - Lecture Notes in Computer Science
Discovery Science
22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings
Häftad, Engelska, 2019
900 kr
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This book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019.The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series.
E-bok
Engelska, 20191 141 kr
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This book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019.
The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series.
Del 16090 - Lecture Notes in Computer Science
Discovery Science
28th International Conference, DS 2025, Ljubljana, Slovenia, September 23–25, 2025, Proceedings
Häftad, Engelska, 2025
891 kr
Skickas inom 5-8 vardagar
The DS 2025 constitutes the proceedings of the 28th International Conference on Discovery Science, DS 2025, which took place in Ljubljana, Slovenia, during September 22–26, 2025.The 38 full papers presented in this volume were carefully reviewed and selected from 86 submissions. They were organized in topical sections as follows: the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, and intelligent data analysis, and big data analytics, as well as their application in various domains.
E-bok
Engelska, 20251 077 kr
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The DS 2025 constitutes the proceedings of the 28th International Conference on Discovery Science, DS 2025, which took place in Ljubljana, Slovenia, during September 22–26, 2025.The 38 full papers presented in this volume were carefully reviewed and selected from 86 submissions. They were organized in topical sections as follows: the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, and intelligent data analysis, and big data analytics, as well as their application in various domains.
Del 8777 - Lecture Notes in Computer Science
Discovery Science
17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings
Häftad, Engelska, 2014
565 kr
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This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.
E-bok
PDF, Engelska, 2014708 kr
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This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.
Del 10535 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II
Häftad, Engelska, 2018
565 kr
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The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows:Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.Part III: applied data science track; nectar track; and demo track.
E-bok
Engelska, 2017734 kr
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The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.
The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.
Del 10534 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I
Häftad, Engelska, 2017
565 kr
Skickas inom 10-15 vardagar
The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows:Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.Part III: applied data science track; nectar track; and demo track.
E-bok
Engelska, 2017734 kr
Läs direkt efter köp
The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.
The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.
The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.
Del 10536 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III
Häftad, Engelska, 2017
565 kr
Skickas inom 10-15 vardagar
The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows:Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.Part III: applied data science track; nectar track; and demo track.
E-bok
Engelska, 2017708 kr
Läs direkt efter köp
The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.
The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.
E-bok
PDF, Engelska, 2003708 kr
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This volume has its origins in the ?rst Learning Language in Logic (LLL) wo- shop which took place on 30 June 1999 in Bled, Slovenia immediately after the Ninth International Workshop on Inductive Logic Programming (ILP’99) and the Sixteenth International Conference on Machine Learning (ICML’99). LLL is a research area lying at the intersection of computational linguistics, machine learning, and computational logic. As such it is of interest to all those working in these three ?elds. I am pleased to say that the workshop attracted subm- sions from both the natural language processing (NLP) community and the ILP community, re?ecting the essentially multi-disciplinary nature of LLL. Eric Brill and Ray Mooney were invited speakers at the workshop and their contributions to this volume re?ect the topics of their stimulating invited talks. After the workshop authors were given the opportunity to improve their papers, the results of which are contained here. However, this volume also includes a substantial amount of two sorts of additional material. Firstly, since our central aim is to introduce LLL work to the widest possible audience, two introductory chapters have been written. Dzeroski, ? Cussens and Manandhar provide an - troduction to ILP and LLL and Thompson provides an introduction to NLP.
Häftad, Engelska, 2000
565 kr
Skickas inom 10-15 vardagar
This volume has its origins in the ?rst Learning Language in Logic (LLL) wo- shop which took place on 30 June 1999 in Bled, Slovenia immediately after the Ninth International Workshop on Inductive Logic Programming (ILP’99) and the Sixteenth International Conference on Machine Learning (ICML’99). LLL is a research area lying at the intersection of computational linguistics, machine learning, and computational logic. As such it is of interest to all those working in these three ?elds. I am pleased to say that the workshop attracted subm- sions from both the natural language processing (NLP) community and the ILP community, re?ecting the essentially multi-disciplinary nature of LLL. Eric Brill and Ray Mooney were invited speakers at the workshop and their contributions to this volume re?ect the topics of their stimulating invited talks. After the workshop authors were given the opportunity to improve their papers, the results of which are contained here. However, this volume also includes a substantial amount of two sorts of additional material. Firstly, since our central aim is to introduce LLL work to the widest possible audience, two introductory chapters have been written. Dzeroski, ? Cussens and Manandhar provide an - troduction to ILP and LLL and Thompson provides an introduction to NLP.
Inbunden, Engelska, 2001
1 123 kr
Skickas inom 10-15 vardagar
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining.This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
E-bok
PDF, Engelska, 2003734 kr
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Häftad, Engelska, 1997
565 kr
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This book constitutes the refereed proceedings of the 7th International Workshop on Inductive Logic Programming, ILP-97, held in Prague, Czech Republic, in September 1997.The volume presents revised versions of nine papers in long version and 17 short papers accepted after a thorough reviewing process. Also included are three invited papers by Usama Fayyad, Jean-Francois Puget, and Georg Gottlob. Among the topics addressed are various logic programming issues, natural language processing, speech processing, abductive learning, data mining, knowledge discovery, and relational database systems.
Häftad, Engelska, 1999
565 kr
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Thisvolumecontains3invitedand24submittedpaperspresentedattheNinth InternationalWorkshoponInductiveLogicProgramming,ILP-99. The24acc- tedpaperswereselectedbytheprogramcommitteefromthe40paperssubmitted toILP-99. Eachpaperwasreviewedbythreereferees,applyinghighreviewing standards. ILP-99washeldinBled,Slovenia,24{27June1999. Itwascollocatedwith theSixteenthInternationalConferenceonMachineLearning,ICML-99,held27{ 30June1999. On27June,ILP-99andICML-99weregivenajointinvitedtalk byJ. RossQuinlanandajointpostersessionwhereallthepapersacceptedat ILP-99andICML-99werepresented. TheproceedingsofICML-99(editedby IvanBratkoandSa soD zeroski)arepublishedbyMorganKaufmann. WewishtothankalltheauthorswhosubmittedtheirpaperstoILP-99,the programcommitteemembersandotherreviewersfortheirhelpinselectinga high-qualityprogram,andtheinvitedspeakers:DaphneKoller,HeikkiMannila, andJ. RossQuinlan. ThanksareduetoTanjaUrban ci candherteamandMajda Zidanskiandherteamfortheorganizationalsupportprovided. Wewishtothank AlfredHofmannandAnnaKramerofSpringer-Verlagfortheircooperationin publishing these proceedings. Finally, we gratefully acknowledge the nancial supportprovidedbythesponsorsofILP-99.April1999 Sa soD zeroski PeterFlach ILP-99ProgramCommittee FrancescoBergadano(UniversityofTorino) HenrikBostr..om(UniversityofStockholm) IvanBratko(UniversityofLjubljana) WilliamCohen(AT&TResearchLabs) JamesCussens(UniversityofYork) LucDeRaedt(UniversityofLeuven) Sa soD zeroski(Jo zefStefanInstitute,co-chair) PeterFlach(UniversityofBristol,co-chair) AlanFrisch(UniversityofYork) KoichiFurukawa(KeioUniversity) RoniKhardon(UniversityofEdinburgh) NadaLavra c(Jo zefStefanInstitute) JohnLloyd(AustralianNationalUniversity) StanMatwin(UniversityofOttawa) RaymondMooney(UniversityofTexas) StephenMuggleton(UniversityofYork) Shan-HweiNienhuys-Cheng(UniversityofRotterdam) DavidPage(UniversityofLouisville) BernhardPfahringer(AustrianResearchInstituteforAI) CelineRouveirol(UniversityofParis) ClaudeSammut(UniversityofNewSouthWales) MicheleSebag(EcolePolytechnique) AshwinSrinivasan(UniversityofOxford) PrasadTadepalli(OregonStateUniversity) StefanWrobel(GMDResearchCenterforInformationTechnology) OrganizationalSupport TheAlbatrossCongressTouristAgency,Bled Center for Knowledge Transfer in Information Technologies, Jo zef Stefan Institute,Ljubljana SponsorsofILP-99 ILPnet2,NetworkofExcellenceinInductiveLogicProgramming COMPULOGNet,EuropeanNetworkofExcellenceinComputationalLogic Jo zefStefanInstitute,Ljubljana LPASoftware,Inc.UniversityofBristol TableofContents I InvitedPapers ProbabilisticRelationalModels D. Koller ...3 InductiveDatabases(Abstract) H. Mannila...14 SomeElementsofMachineLearning(ExtendedAbstract) J. R. Quinlan...15 II ContributedPapers Re nementOperatorsCanBe(Weakly)Perfect L. Badea,M. Stanciu...21 CombiningDivide-and-ConquerandSeparate-and-ConquerforE cientand E ectiveRuleInduction H. Bostr..om,L. Asker...33 Re ningCompleteHypothesesinILP I. Bratko...44 AcquiringGraphicDesignKnowledge withNonmonotonicInductiveLearning K. Chiba,H. Ohwada,F. Mizoguchi...56 MorphosyntacticTaggingofSloveneUsingProgol J. Cussens,S. D zeroski,T. Erjavec ...68 ExperimentsinPredictingBiodegradability S. D zeroski,H. Blockeel,B. Kompare,S. Kramer, B. Pfahringer,W. VanLaer ...80 1BC:AFirst-OrderBayesianClassi er P. Flach,N. Lachiche...92 SortedDownwardRe nement:BuildingBackgroundKnowledge intoaRe nementOperatorforInductiveLogicProgramming A. M. Frisch ...104 AStrongCompleteSchemaforInductiveFunctionalLogicProgramming J. Hern andez-Orallo,M. J. Ram rez-Quintana...116 ApplicationofDi erentLearningMethods toHungarianPart-of-SpeechTagging T. Horv ath,Z. Alexin,T. Gyim othy,S. Wrobel ...128 VIII TableofContents CombiningLAPISandWordNetfortheLearningofLRParserswith OptimalSemanticConstraints D. Kazakov...140 LearningWordSegmentationRulesforTagPrediction D. Kazakov,S. Manandhar,T. Erjavec ...152 ApproximateILPRulesbyBackpropagationNeuralNetwork: AResultonThaiCharacterRecognition B. Kijsirikul,S. Sinthupinyo...162 RuleEvaluationMeasures:AUnifyingView N. Lavra c,P. Flach,B. Zupan...174 ImprovingPart-of-SpeechDisambiguationRulesbyAdding LinguisticKnowledge N. Lindberg,M. Eineborg ...186 OnSu cientConditionsforLearnabilityofLogicProgramsfrom PositiveData E. Martin,A. Sharma ...198 ABoundedSearchSpaceofClausalTheories H. Midelfart...210 DiscoveringNewKnowledgefromGraphData UsingInductiveLogicProgramming T. Miyahara,T. Shoudai,T. Uchida,T. Kuboyama, K. Takahashi,H. Ueda...
Häftad, Engelska, 2007
542 kr
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Advances in technology have enabled the collection of data from scientific observations, simulations, and experiments at an ever-increasing pace. For the scientist and engineer to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated data analysis techniques must be applied to find the useful information in the data. Computational scientific discovery methods can be used to this end: they focus on applying computational methods to automate scientific activities, such as finding laws from observational data. In contrast to mining scientific data, which focuses on building highly predictive models, computational scientific discovery puts a strong emphasis on discovering knowledge represented in formalisms used by scientists and engineers, such as numeric equations and reaction pathways. This state-of-the-art survey provides an introduction to computational approaches to the discovery of scientific knowledge and gives an overview of recent advances in this area, including techniques and applications in environmental and life sciences. The 15 articles presented are partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001. More representative coverage of recent research in computational scientific discovery is achieved by a significant number of additional invited contributions.
E-bok
PDF, Engelska, 2007708 kr
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Advances in technology have enabled the collection of data from scientific observations, simulations, and experiments at an ever-increasing pace. For the scientist and engineer to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated data analysis techniques must be applied to find the useful information in the data. Computational scientific discovery methods can be used to this end: they focus on applying computational methods to automate scientific activities, such as finding laws from observational data. In contrast to mining scientific data, which focuses on building highly predictive models, computational scientific discovery puts a strong emphasis on discovering knowledge represented in formalisms used by scientists and engineers, such as numeric equations and reaction pathways.
This state-of-the-art survey provides an introduction to computational approaches to the discovery of scientific knowledge and gives an overview of recent advances in this area, including techniques and applications in environmental and life sciences. The 15 articles presented are partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001. More representative coverage of recent research in computational scientific discovery is achieved by a significant number of additional invited contributions.
Häftad, Engelska, 2007
565 kr
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This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
E-bok
PDF, Engelska, 2007708 kr
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Häftad, Engelska, 2010
1 125 kr
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As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining.This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
E-bok
PDF, Engelska, 20131 379 kr
Läs direkt efter köp
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining.This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.