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Encyclopedia of Machine Learning and Data Mining

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Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Abduction
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    Chapter 6 Adaptive Resonance Theory
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    Chapter 8 Evaluation of Learning Algorithms
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    Chapter 16 Autonomous Helicopter Flight Using Reinforcement Learning
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    Chapter 17 Average-Reward Reinforcement Learning
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    Chapter 21 Bayes’ Rule
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    Chapter 28 Bias-Variance Trade-offs: Novel Applications
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    Chapter 30 Biomedical Informatics
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    Chapter 31 Boltzmann Machines
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    Chapter 33 Cascade-Correlation
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    Chapter 34 Case-Based Reasoning
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    Chapter 35 Categorical Data Clustering
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    Chapter 36 Causality
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    Chapter 41 Clustering from Data Streams
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    Chapter 44 Collective Classification
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    Chapter 45 Complexity in Adaptive Systems
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    Chapter 46 Complexity of Inductive Inference
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    Chapter 47 Computational Complexity of Learning
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    Chapter 50 Confusion Matrix
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    Chapter 52 Connections Between Inductive Inference and Machine Learning
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    Chapter 57 Covariance Matrix
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    Chapter 62 Data Preparation
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    Chapter 64 Decision List
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    Chapter 65 Decision Lists and Decision Trees
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    Chapter 66 Decision Tree
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    Chapter 67 Deep Belief Nets
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    Chapter 70 Density-Based Clustering
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    Chapter 71 Dimensionality Reduction
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    Chapter 75 Document Classification
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    Chapter 77 Dynamic Programming
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    Chapter 79 Empirical Risk Minimization
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    Chapter 81 Entity Resolution
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    Chapter 82 Epsilon Cover
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    Chapter 83 Epsilon Nets
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    Chapter 85 Error Rate
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    Chapter 86 Evolutionary Clustering
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    Chapter 87 Evolutionary Computation in Economics
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    Chapter 88 Evolutionary Computation in Finance
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    Chapter 89 Evolutionary Computational Techniques in Marketing
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    Chapter 90 Evolutionary Feature Selection and Construction
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    Chapter 92 Evolutionary Games
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    Chapter 93 Evolutionary Kernel Learning
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    Chapter 94 Evolutionary Robotics
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    Chapter 95 Expectation Propagation
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    Chapter 96 Explanation-Based Learning
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    Chapter 97 Explanation-Based Learning for Planning
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    Chapter 100 Feature Construction in Text Mining
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    Chapter 101 Feature Selection
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    Chapter 102 Feature Selection in Text Mining
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    Chapter 103 First-Order Logic
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    Chapter 107 Gaussian Distribution
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    Chapter 108 Gaussian Process
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    Chapter 109 Gaussian Process Reinforcement Learning
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    Chapter 113 Generative and Discriminative Learning
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    Chapter 115 Grammatical Inference
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    Chapter 119 Graphical Models
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    Chapter 124 Hidden Markov Models
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    Chapter 126 Higher-Order Logic
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    Chapter 127 Hopfield Network
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    Chapter 130 Incremental Learning
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    Chapter 134 Inductive Inference
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    Chapter 135 Inductive Logic Programming
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    Chapter 137 Inductive Programming
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    Chapter 138 Inductive Transfer
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    Chapter 142 Inverse Reinforcement Learning
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    Chapter 144 Kernel Methods
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    Chapter 275 Covering Algorithm
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    Chapter 285 Decision Stump
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    Chapter 303 Divide-and-Conquer Learning
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    Chapter 344 Expectation Maximization Clustering
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    Chapter 376 Genetic Programming
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    Chapter 431 K-Means Clustering
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    Chapter 432 K-Medoids Clustering
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    Chapter 433 K-Way Spectral Clustering
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    Chapter 449 Lazy Learning
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    Chapter 465 Learning Graphical Models
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    Chapter 468 Learning Models of Biological Sequences
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    Chapter 473 Least-Squares Reinforcement Learning Methods
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    Chapter 480 Linear Discriminant
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    Chapter 481 Linear Regression
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    Chapter 486 Link Prediction
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    Chapter 493 Locally Weighted Regression for Control
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    Chapter 509 Machine Learning and Game Playing
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    Chapter 511 Manhattan Distance
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    Chapter 525 Maximum Entropy Models for Natural Language Processing
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    Chapter 532 Mean Shift
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    Chapter 543 Metalearning
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    Chapter 547 Minimum Message Length
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    Chapter 552 Mixture Model
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    Chapter 555 Model Evaluation
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    Chapter 558 Model Trees
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    Chapter 561 Model-Based Reinforcement Learning
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    Chapter 568 Multi-agent Learning I: Problem Definition
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    Chapter 569 Multi-agent Learning II: Algorithms
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    Chapter 578 Multiple-Instance Learning
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    Chapter 581 Naïve Bayes
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    Chapter 594 Neuroevolution
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    Chapter 595 Neuron
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    Chapter 614 Occam’s Razor
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    Chapter 618 Online Learning
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    Chapter 623 Rule Set
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    Chapter 631 PAC Learning
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    Chapter 637 Partitional Clustering
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    Chapter 642 Phase Transitions in Machine Learning
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    Chapter 646 Policy Gradient Methods
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    Chapter 658 Precision
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    Chapter 659 Precision and Recall
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    Chapter 667 Preference Learning
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    Chapter 679 Programming by Demonstration
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    Chapter 686 Propositionalization
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    Chapter 687 Pruning
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    Chapter 689 Q-Learning
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    Chapter 692 Quality Threshold Clustering
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    Chapter 694 Query-Based Learning
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    Chapter 698 Radial Basis Function Networks
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    Chapter 705 Rank Correlation
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    Chapter 712 Record Linkage
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    Chapter 716 Regression
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    Chapter 717 Regression Trees
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    Chapter 718 Regularization
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    Chapter 720 Reinforcement Learning
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    Chapter 726 Relational Reinforcement Learning
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    Chapter 731 Reservoir Computing
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    Chapter 738 Robot Learning
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    Chapter 739 ROC Analysis
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    Chapter 744 Rule Learning
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    Chapter 750 Search Engines: Applications of ML
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    Chapter 758 Sensitivity and Specificity
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    Chapter 766 Similarity Measures
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    Chapter 768 Simple Recurrent Network
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    Chapter 778 Speedup Learning
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    Chapter 793 Stochastic Finite Learning
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    Chapter 799 Structural Risk Minimization
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    Chapter 808 Supervised Descriptive Rule Induction
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    Chapter 810 Support Vector Machines
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    Chapter 831 Text Mining
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    Chapter 833 Text Mining for News and Blogs Analysis
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    Chapter 835 Text Mining for the Semantic Web
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    Chapter 837 Text Visualization
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    Chapter 843 Topology of a Neural Network
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    Chapter 844 Trace-Based Programming
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    Chapter 867 Universal Learning Theory
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    Chapter 876 Value Function Approximation
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    Chapter 881 VC Dimension
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    Chapter 886 Weight
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    Chapter 891 Online Controlled Experiments and A/B Testing
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    Chapter 892 Learning Using Privileged Information
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    Chapter 893 Learning to Rank
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    Chapter 894 Minimum Description Length Principle
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    Chapter 900 Classifier Calibration
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    Chapter 901 Event Extraction from Media Texts
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    Chapter 902 Table Extraction from Text Documents
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    Chapter 903 Semantic Annotation of Text Using Open Semantic Resources
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    Chapter 905 Opinion Stream Mining
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    Chapter 906 Topic Models for NLP Applications
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    Chapter 907 Sentiment Analysis and Opinion Mining
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    Chapter 908 Stream Classification
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    Chapter 909 Deep Learning
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    Chapter 910 Multi-label Learning
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    Chapter 911 Uplift Modeling
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    Chapter 912 Anomaly Detection
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    Chapter 913 Quantum Machine Learning
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    Chapter 914 Classification Rule
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    Chapter 915 Class Binarization
Attention for Chapter 902: Table Extraction from Text Documents
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Chapter title
Table Extraction from Text Documents
Chapter number 902
Book title
Encyclopedia of Machine Learning and Data Mining
Published by
Springer US, August 2016
DOI 10.1007/978-1-4899-7502-7_902-1
Book ISBNs
978-1-4899-7502-7
Authors

James Hodson

Editors

Claude Sammut, Geoffrey I. Webb

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%