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Encyclopedia of Machine Learning and Data Science

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 the...

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Bibliographic Details
Other Authors: Phung, Dinh (Editor), Webb, Geoffrey I. (Editor), Sammut, Claude (Editor)
Format: eBook
Language:English
Published: New York, NY Springer US 2020, 2020
Subjects:
Pattern Recognition
Artificial Intelligence
Statistics 
Data Mining
Data Mining And Knowledge Discovery
Statistics And Computing/statistics Programs
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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
  • 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
  • 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

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