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Encyclopedia of Machine Learning and Data Mining, 2 Teile

BuchGebunden
1335 Seiten
Englisch
Springererschienen am15.03.20172. Aufl.
This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining.mehr
Verfügbare Formate
BuchGebunden
EUR1.108,50
BuchGebunden
EUR962,50

Produkt

KlappentextThis authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining.
Zusammenfassung
Presents 800 entries covering key concepts and terms in the broad field of machine learning

Updates and informs through in-depth essays and definitions, historical background, key applications, and bibliographies

Supports quick and efficient discovery of information through extensive cross-references

Opens the field to those inquiring into this fast-growing area of research

Includes supplementary material: sn.pub/extras
Details
ISBN/GTIN978-1-4899-7685-7
ProduktartBuch
EinbandartGebunden
Verlag
Erscheinungsjahr2017
Erscheinungsdatum15.03.2017
Auflage2. Aufl.
Seiten1335 Seiten
SpracheEnglisch
IllustrationenXVII, 1335 p. 263 illus., 83 illus. in color. In 2 volumes, not available separately.
Artikel-Nr.15640127

Inhalt/Kritik

Inhaltsverzeichnis
Abduction.-  Adaptive Resonance Theory.-  Anomaly Detection.-  Bayes Rule.-  Case-Based Reasoning.-  Categorical Data Clustering.-  Causality.-  Clustering from Data Streams.-  Complexity in Adaptive Systems.-  Complexity of Inductive Inference.-  Computational Complexity of Learning.-  Confusion Matrix.-  Connections Between Inductive Inference and Machine Learning.-  Covariance Matrix.-  Decision List.-  Decision Lists and Decision Trees.-  Decision Tree.-  Deep Learning.-  Density-Based Clustering.-  Dimensionality Reduction.-  Document Classification.-  Dynamic Memory Model.-  Empirical Risk Minimization.-  Error Rate.-  Event Extraction from Media Texts.-  Evolutionary Clustering.-  Evolutionary Computation in Economics.-  Evolutionary Computation in Finance.-  Evolutionary Computational Techniques in Marketing.-  Evolutionary Feature Selection and Construction.-  Evolutionary Kernel Learning.-  Evolutionary Robotics.-  Expectation Maximization Clustering.-  Expectation Propagation.-  Feature Construction in Text Mining.-  Feature Selection.-  Feature Selection in Text Mining.-  Gaussian Distribution.-  Gaussian Process.-  Generative and Discriminative Learning.-  Grammatical Inference.-  Graphical Models.-  Hidden Markov Models.-  Inductive Inference.-  Inductive Logic Programming.-  Inductive Programming.-  Inductive Transfer.-  Inverse Reinforcement Learning.-  Kernel Methods.-  K-Means Clustering.-  K-Medoids Clustering.-  K-Way Spectral Clustering.-  Learning Algorithm Evaluation.-  Learning Graphical Models.-  Learning Models of Biological Sequences.-  Learning to Rank.-  Learning Using Privileged Information.-  Linear Discriminant.-  Linear Regression.-  Locally Weighted Regression for Control.-  Machine Learning and Game Playing.-  Manhattan Distance.-  Maximum Entropy Models for Natural Language Processing.-  Mean Shift.-  Metalearning.-  Minimum Description Length Principle.-  Minimum Message Length.-  Mixture Model.-  Model Evaluation.-  Model Trees.-  Multi Label Learning.-  Naïve Bayes.-  Occam's Razor.-  Online Controlled Experiments and A/B Testing.-  Online Learning.-  Opinion Stream Mining .-  PAC Learning.-  Partitional Clustering.-  Phase Transitions in Machine Learning.mehr

Schlagworte

Autor

Claude Sammut is a Professor of Computer Science and Engineering at the University of New South Wales, Australia, and Head of the Artificial Intelligence Research Group. He is the UNSW node Director of the ARC Centre of Excellence for Autonomous Systems and a member of the joint ARC/NH&MRC project on Thinking Systems. He is on the editorial boards of the Journal of Machine Learning Research, the Machine Learning Journal and New Generation Computing, and was the chairman of the 2007 International Conference on Machine Learning.
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Herausgegeben:Sammut, Claude