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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 3 Accuracy
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    Chapter 5 Actions
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    Chapter 6 Adaptive Resonance Theory
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    Chapter 7 Active Learning Theory
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    Chapter 10 Adaptive Real-Time Dynamic Programming
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    Chapter 13 Agent
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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 18 Algorithm Evaluation
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    Chapter 21 Bayes’ Rule
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    Chapter 22 Ant Colony Optimization
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    Chapter 23 Anytime Algorithm
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    Chapter 27 Apriori Algorithm
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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 37 Assertion
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    Chapter 38 Association Rule
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    Chapter 40 Associative Reinforcement Learning
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    Chapter 41 Clustering from Data Streams
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    Chapter 43 Attribute-Value Learning
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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 48 Averaged One-Dependence Estimators
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    Chapter 50 Confusion Matrix
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    Chapter 51 Backpropagation
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    Chapter 52 Connections Between Inductive Inference and Machine Learning
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    Chapter 53 Bake-Off
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    Chapter 57 Covariance Matrix
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    Chapter 58 Batch Learning
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    Chapter 59 Baum-Welch Algorithm
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    Chapter 62 Data Preparation
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    Chapter 63 Bayesian Methods
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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 68 Beam Search
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    Chapter 69 Behavioral Cloning
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    Chapter 70 Density-Based Clustering
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    Chapter 71 Dimensionality Reduction
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    Chapter 72 Bias
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    Chapter 73 Bias Specification Language
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    Chapter 74 Bias Variance Decomposition
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    Chapter 75 Document Classification
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    Chapter 78 Bilingual Lexicon Extraction
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    Chapter 80 Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity
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    Chapter 84 Boosting
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    Chapter 91 Candidate-Elimination Algorithm
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    Chapter 98 Categorical Attribute
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    Chapter 110 Class Imbalance Problem
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    Chapter 111 Classification
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    Chapter 112 Classification Algorithms
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    Chapter 116 Clause
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    Chapter 117 Clause Learning
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    Chapter 118 Click-Through Rate (CTR)
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    Chapter 121 Cluster Editing
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    Chapter 122 Cluster Ensembles
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    Chapter 146 Competitive Learning
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    Chapter 150 Compositional Coevolution
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    Chapter 153 Concept Drift
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    Chapter 154 Concept Learning
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    Chapter 155 Conditional Random Field
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    Chapter 156 Confirmation Theory
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    Chapter 158 Conjunctive Normal Form
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    Chapter 162 Consensus Clustering
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    Chapter 163 Constrained Clustering
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    Chapter 164 Constraint-Based Mining
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    Chapter 165 Constructive Induction
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    Chapter 167 Content-Based Filtering
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    Chapter 171 Continual Learning
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    Chapter 172 Continuous Attribute
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    Chapter 173 Contrast Set Mining
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    Chapter 176 Correlation Clustering
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    Chapter 178 Cost
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    Chapter 181 Cost-Sensitive Learning
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    Chapter 185 Credit Assignment
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    Chapter 186 Cross-Language Document Categorization
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    Chapter 187 Cross-Language Information Retrieval
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    Chapter 188 Cross-Language Question Answering
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    Chapter 189 Cross-Lingual Text Mining
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    Chapter 190 Cross-Validation
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    Chapter 191 Cumulative Learning
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    Chapter 192 Curse of Dimensionality
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    Chapter 196 Data Set
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    Chapter 197 DBN
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    Chapter 198 Decision Epoch
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    Chapter 201 Decision Rule
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    Chapter 203 Decision Threshold
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    Chapter 206 Deductive Learning
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    Chapter 210 Density Estimation
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    Chapter 213 Detail
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    Chapter 215 Digraphs
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    Chapter 219 Dirichlet Process
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    Chapter 220 Discrete Attribute
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    Chapter 221 Discretization
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    Chapter 222 Discriminative Learning
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    Chapter 223 Disjunctive Normal Form
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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 900 Classifier Calibration
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    Chapter 909 Deep Learning
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    Chapter 912 Active Learning
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    Chapter 914 Classification Rule
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    Chapter 915 Class Binarization
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    Chapter 916 Active Learning
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    Chapter 917 Adaboost
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    Chapter 918 Area Under Curve
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    Chapter 919 Artificial Immune Systems
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    Chapter 920 Artificial Life
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    Chapter 921 Artificial Neural Networks
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    Chapter 922 Artificial Societies
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    Chapter 923 Attribute
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    Chapter 925 Bagging
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    Chapter 926 Basket Analysis
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    Chapter 927 Bayesian Network
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    Chapter 928 Bayesian Nonparametric Models
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    Chapter 929 Bayesian Reinforcement Learning
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    Chapter 930 Bellman Equation
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    Chapter 932 Bias-Variance-Covariance Decomposition
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    Chapter 934 Blog Mining
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    Chapter 936 Bottom Clause
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    Chapter 937 Breakeven Point
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    Chapter 938 Cannot-Link Constraint
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    Chapter 940 Class
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    Chapter 941 Classifier Systems
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    Chapter 942 Clonal Selection
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    Chapter 943 Clustering
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    Chapter 944 Coevolutionary Learning
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    Chapter 945 Collaborative Filtering
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    Chapter 946 Comparable Corpus
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    Chapter 977 Bootstrap Sampling
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    Chapter 979 Data Enrichment
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    Chapter 10025 AUC
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    Chapter 100003 ACO
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    Chapter 100004 Adaptive Control Processes
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    Chapter 100005 Adaptive Learning
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    Chapter 100007 Agent-Based Computational Models
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    Chapter 100008 Agent-Based Modeling and Simulation
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    Chapter 100009 Agent-Based Simulation Models
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    Chapter 100010 AIS
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    Chapter 100012 Analogical Reasoning
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    Chapter 100013 Analysis of Text
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    Chapter 100015 Analytical Learning
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    Chapter 100016 AODE
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    Chapter 100017 Apprenticeship Learning
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    Chapter 100018 Approximate Dynamic Programming
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    Chapter 100019 Architecture
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    Chapter 100020 ARL
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    Chapter 100021 ARTDP
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    Chapter 100022 Assessment of Model Performance
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    Chapter 100023 Associative Bandit Problem
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    Chapter 100026 Authority Control
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    Chapter 100027 Average-Cost Neuro-Dynamic Programming
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    Chapter 100028 Average-Cost Optimization
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    Chapter 100029 Average-Payoff Reinforcement Learning
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    Chapter 100030 Backprop
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    Chapter 100031 Bandit Problem with Side Information
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    Chapter 100032 Bandit Problem with Side Observations
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    Chapter 100033 Basic Lemma
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    Chapter 100034 Bayes Adaptive Markov Decision Processes
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    Chapter 100035 Bayes Net
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    Chapter 100036 Bayes’ Theorem
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    Chapter 100038 Bayesian Model Averaging
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    Chapter 100039 Belief State Markov Decision Processes
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    Chapter 100040 Binning
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    Chapter 100041 Bounded Differences Inequality
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    Chapter 100042 BP
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    Chapter 100043 Cascor
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    Chapter 100044 Case
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    Chapter 100045 Case-Based Learning
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    Chapter 100046 Categorization
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    Chapter 100048 Category
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    Chapter 100049 Causal Discovery
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    Chapter 100050 CC
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    Chapter 100051 Certainty Equivalence Principle
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    Chapter 100052 Characteristic
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    Chapter 100053 Citation or Reference Matching (When Applied to Bibliographic Data)
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    Chapter 100054 City Block Distance
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    Chapter 100055 Classification Learning
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    Chapter 100056 Classification Tree
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    Chapter 100057 Closest Point
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    Chapter 100058 Cluster Initialization
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    Chapter 100059 Cluster Optimization
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    Chapter 100060 Clustering Aggregation
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    Chapter 100061 Clustering Ensembles
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    Chapter 100062 Clustering of Nonnumerical Data
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    Chapter 100063 Clustering with Advice
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    Chapter 100064 Clustering with Constraints
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    Chapter 100065 Clustering with Qualitative Information
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    Chapter 100066 Clustering with Side Information
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    Chapter 100067 Coevolution
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    Chapter 100068 Coevolutionary Computation
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    Chapter 100069 Collection
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    Chapter 100070 Commercial Email Filtering
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    Chapter 100071 Committee Machines
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    Chapter 100072 Community Detection
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    Chapter 100073 Comparison Training
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    Chapter 100074 Competitive Coevolution
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    Chapter 100075 Complex Adaptive System
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    Chapter 100077 Computational Discovery of Quantitative Laws
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    Chapter 100078 Connection Strength
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    Chapter 100079 Connectivity
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    Chapter 100080 Constraint Classification
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    Chapter 100081 Content Match
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    Chapter 100082 Content-Based Recommending
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    Chapter 100083 Context-Sensitive Learning
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    Chapter 100084 Contextual Advertising
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    Chapter 100086 Cooperative Coevolution
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    Chapter 100087 Co-reference Resolution
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    Chapter 100089 Correlation-Based Learning
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    Chapter 100091 Cost Function
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    Chapter 100092 Cost-Sensitive Classification
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    Chapter 100093 Cost-to-Go Function Approximation
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    Chapter 100094 Co-training
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    Chapter 100096 Data Cleansing
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    Chapter 100097 Data Linkage
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    Chapter 100098 Data Matching
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    Chapter 100099 Data mining on Text
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    Chapter 100100 Data Preprocessing
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    Chapter 100101 Data Reconciliation
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    Chapter 100102 Data Wrangling
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    Chapter 100103 Decision Trees for Regression
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    Chapter 100105 Deduplication
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    Chapter 100106 Deduplication or Duplicate Detection (When Applied to One Database Only)
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    Chapter 100107 Deep Belief Networks
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    Chapter 100109 Dependency Directed Backtracking
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    Chapter 100110 Diagonal Matrix
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    Chapter 100111 Differential Prediction
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    Chapter 100112 Dimensionality Reduction on Text via Feature Selection
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    Chapter 100113 Directed Graphs
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    Chapter 100115 Distance
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    Chapter 100116 Distance Functions
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    Chapter 100117 Distance Measures
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    Chapter 100118 Distance Metrics
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    Chapter 100119 Distribution-Free Learning
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    Chapter 100120 Document Categorization
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    Chapter 100121 Domain Adaptation
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    Chapter 100504 Absolute Error Loss
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    Chapter 100505 Adaptive System
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    Chapter 100506 Attribute Selection
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    Chapter 100507 A/B Testing
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    Chapter 100508 AQ
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    Chapter 100509 ART
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    Chapter 100514 Data Augmentation
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    Chapter 100515 Data Cleaning
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    Chapter 100516 Data Integration
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    Chapter 100517 Data Scrubbing
Attention for Chapter 100119: Distribution-Free Learning
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Chapter title
Distribution-Free Learning
Chapter number 100119
Book title
Encyclopedia of Machine Learning and Data Mining
Published by
Springer US, January 2017
DOI 10.1007/978-1-4899-7687-1_100119
Book ISBNs
978-1-4899-7685-7, 978-1-4899-7687-1
Editors

Claude Sammut, Geoffrey I. Webb

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 > Ph. D. Student 1 50%
Lecturer 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Agricultural and Biological Sciences 1 50%