Machine Learning and Knowledge Discovery in Databases (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
740
Utgivningsdatum
2019-01-18
Upplaga
1st ed. 2019
Förlag
Springer Nature Switzerland AG
Medarbetare
Berlingerio, Michele (ed.), Bonchi, Francesco (ed.), Gärtner, Thomas (ed.), Hurley, Neil (ed.), Ifrim, Georgiana (ed.)
Illustratör/Fotograf
Bibliographie 190 schwarz-weiße Abbildungen
Illustrationer
159 Illustrations, color; 292 Illustrations, black and white; XXXVIII, 740 p. 451 illus., 159 illus.
Dimensioner
234 x 156 x 39 mm
Vikt
1071 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030109240
Machine Learning and Knowledge Discovery in Databases (häftad)

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Häftad Engelska, 2019-01-18
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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.
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Innehållsförteckning

Adversarial Learning.- Image Anomaly Detection with Generative Adversarial Networks.- Image-to-Markup Generation via Paired Adversarial Learning.- Toward an Understanding of Adversarial Examples in Clinical Trials.- ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector.- Anomaly and Outlier Detection.- GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid.- Incorporating Privileged Information to Unsupervised Anomaly Detection.- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space.- Beyond Outlier Detection: LookOut for Pictorial Explanation.- Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier Features.- Group Anomaly Detection using Deep Generative Models.- Applications.- A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements.- Face-Cap: Image Captioning using Facial Expression Analysis.- Pedestrian Trajectory Prediction with Structured Memory Hierarchies.- Classification.- Multiple Instance Learning with Bag-level Randomized Trees.- One-class Quantification.- Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study.- Ordinal Label Proportions.- AWX: An Integrated Approach to Hierarchical-Multilabel Classification.- Clustering and Unsupervised Learning.- Clustering in the Presence of Concept Drift.- Time Warp Invariant Dictionary Learning for Time Series Clustering.- How Your Supporters and Opponents Define Your Interestingness.- Deep Learning.- Efficient Decentralized Deep Learning by Dynamic Model Averaging.- Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems.- Towards Efficient Forward Propagation on Resource-Constrained Systems.- Auxiliary Guided Autoregressive Variational Autoencoders.- Cooperative Multi-Agent Policy Gradient.- Parametric t-Distributed Stochastic Exemplar-centered Embedding.- Joint autoencoders: a flexible meta-learning framework.- Privacy Preserving Synthetic Data Release Using Deep Learning.- On Finer Control of Information Flow in LSTMs.- MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes.- Ontology alignment based on word embedding and random forest classification.- Domain Adaption in One-Shot Learning.- Ensemble Methods.- Axiomatic Characterization of AdaBoost and the Multiplicative Weight Update Procedure.- Modular Dimensionality Reduction.- Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles.- MetaBags: Bagged Meta-Decision Trees for Regression.- Evaluation.- Visualizing the Feature Importance for Black Box Models.- Efficient estimation of AUC in a sliding window.- Controlling and visualizing the precision-recall tradeoff for external performance indices.- Evaluation Procedures for Forecasting with Spatio-Temporal Data.- A Blended Metric for Multi-label Optimisation and Evaluation.