Encyclopedia of Machine Learning and Data Science / edited by Dinh Phung, Geoffrey I. Webb, Claude Sammut.

Format:

Book

Contributor:

Phung, Dinh, editor.

Webb, Geoffrey I., editor.

Sammut, Claude, 1956- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Statistics.

Pattern perception.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Statistics and Computing/Statistics Programs.

Pattern Recognition.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Statistics and Computing/Statistics Programs.

Pattern Recognition.

Physical Description:

1 online resource (XXV, 1975 pages) : 600 illustrations

Contained In:

Springer Nature Living Reference

Place of Publication:

New York, NY : Springer US : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

This authoritative, expanded and updated third 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 1000 entries - over 200 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 Science include recent developments in Deep Learning, 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.

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.

ISBN:

978-1-4899-7502-7

9781489975027

Access Restriction:

Restricted for use by site license.