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Encyclopedia of Machine Learning and Data Mining 2nd ed. 2017 [Multiple-component retail product]

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  • Formaat: Multiple-component retail product, 1335 pages, kõrgus x laius: 254x178 mm, 83 Illustrations, color; 180 Illustrations, black and white; XVII, 1335 p. 263 illus., 83 illus. in color. Print + eReference. In 2 volumes, not available separately., 2 Items, Contains 1 Hardback and 1 Digital (delivered electronically)
  • Ilmumisaeg: 18-Mar-2017
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1489976868
  • ISBN-13: 9781489976864
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Encyclopedia of Machine Learning and Data Mining 2nd ed. 2017
  • Formaat: Multiple-component retail product, 1335 pages, kõrgus x laius: 254x178 mm, 83 Illustrations, color; 180 Illustrations, black and white; XVII, 1335 p. 263 illus., 83 illus. in color. Print + eReference. In 2 volumes, not available separately., 2 Items, Contains 1 Hardback and 1 Digital (delivered electronically)
  • Ilmumisaeg: 18-Mar-2017
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1489976868
  • ISBN-13: 9781489976864
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781489976864.html
Märksõnad:
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.  A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.
Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others.  Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.
The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.


Arvustused

The topics covered in the revised edition include applications, data mining, evolutionary computation, graph mining, information theory, learning and logic, pattern mining, reinforcement learning, relational mining, statistical learning, and text mining. I recommend the encyclopedia as a valuable resource for libraries . (S. V. Nagaraj, Computing Reviews, January, 2018)









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.
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.





Geoffrey I. Webb is research professor in the faculty of Information Technology at Monash University, Melbourne, Australia. He has published more than 150 scientific papers and is the author of the data mining software package Magnum Opus. His research areas include strategies for strengthening the Naïve Bayes machine learning technique, K-optimal pattern discovery, and work on Occams razor. He is editor-in-chief of Springers Data Mining and Knowledge Discovery journal, as well as being on the editorial board of Machine Learning.