Encyclopedia of Machine Learning and Data Mining 2nd Edition by Claude Sammut,Geoffrey I Webb ISBN 1489976868 978-1489976864

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Product details:

ISBN 10: 1489976868
ISBN 13: 978-1489976864
Author:  Claude Sammut,Geoffrey I Webb

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.

Table of contents:

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

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