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Book Creation by Wiki

Machine Learning

Introduction and Main Principles: Machine learning; Data analysis; Occam's razor; Curse of dimensionality; No free lunch theorem; Accuracy paradox; Overfitting; Regularization (machine learning); Inductive bias; Data dredging; Ugly duckling theorem; Uncertain data
Background and Preliminaries
Knowledge discovery in Databases: Knowledge discovery; Data mining; Predictive analytics; Predictive modelling; Business intelligence; Reactive business intelligence; Business analytics; Reactive business intelligence; Pattern recognition
Reasoning: Abductive reasoning; Inductive reasoning; First-order logic; Inductive logic programming; Reasoning system; Case-based reasoning; Textual case based reasoning; Causality
Search Methods: Nearest neighbor search; Stochastic gradient descent; Beam search; Best-first search; Breadth-first search; Hill climbing; Grid search; Brute-force search; Depth-first search; Tabu search; Anytime algorithm
Statistics: Exploratory data analysis; Covariate; Statistical inference; Algorithmic inference; Bayesian inference; Base rate; Bias (statistics); Gibbs sampling; Cross-entropy method; Latent variable; Maximum likelihood; Maximum a posteriori estimation; Expectation–maximization algorithm; Expectation propagation; Kullback–Leibler divergence; Generative model
Main Learning Paradigms: Supervised learning; Unsupervised learning; Active learning (machine learning); Reinforcement learning; Multi-task learning; Transduction; Explanation-based learning; Offline learning; Online learning model; Online machine learning; Hyperparameter optimization
Classification Tasks: Classification in machine learning; Concept class; Features (pattern recognition); Feature vector; Feature space; Concept learning; Binary classification; Decision boundary; Multiclass classification; Class membership probabilities; Calibration (statistics); Concept drift; Prior knowledge for pattern recognition
Online Learning: Margin Infused Relaxed Algorithm
Semi-supervised learning: Semi-supervised learning; One-class classification; Coupled pattern learner
Lazy learning and nearest neighbors: Lazy learning; Eager learning; Instance-based learning; Cluster assumption; K-nearest neighbor algorithm; IDistance; Large margin nearest neighbor
Decision Trees: Decision tree learning; Decision stump; Pruning (decision trees); Mutual information; Adjusted mutual information; Information gain ratio; Information gain in decision trees; ID3 algorithm; C4.5 algorithm; CHAID; Information Fuzzy Networks; Grafting (decision trees); Incremental decision tree; Alternating decision tree; Logistic model tree; Random forest
Linear Classifiers: Linear classifier; Margin (machine learning); Margin classifier; Soft independent modelling of class analogies
Statistical classification: Statistical classification; Probability matching; Discriminative model; Linear discriminant analysis; Multiclass LDA; Multiple discriminant analysis; Optimal discriminant analysis; Fisher kernel; Discriminant function analysis; Multilinear subspace learning; Quadratic classifier; Variable kernel density estimation; Category utility
Evaluation of Classification Models: Data classification (business intelligence); Training set; Test set; Synthetic data; Cross-validation (statistics); Loss function; Hinge loss; Generalization error; Type I and type II errors; Sensitivity and specificity; Precision and recall; F1 score; Confusion matrix; Matthews correlation coefficient; Receiver operating characteristic; Lift (data mining); Stability in learning
Feature Creation and Optimization: Data Pre-processing; Discretization of continuous features; Feature engineering; Feature selection; Feature extraction; Dimension reduction; Principal component analysis; Multilinear principal-component analysis; Multifactor dimensionality reduction; Targeted projection pursuit; Multidimensional scaling; Nonlinear dimensionality reduction; Kernel principal component analysis; Kernel eigenvoice; Gramian matrix; Gaussian process; Kernel adaptive filter; Isomap; Manifold alignment; Diffusion map; Elastic map; Locality-sensitive hashing; Spectral clustering; Minimum redundancy feature selection
Clustering: Cluster analysis; K-means clustering; K-means++; K-medians clustering; K-medoids; DBSCAN; Fuzzy clustering; BIRCH (data clustering); Canopy clustering algorithm; Cluster-weighted modeling; Clustering high-dimensional data; Cobweb (clustering); Complete-linkage clustering; Constrained clustering; Correlation clustering; CURE data clustering algorithm; Data stream clustering; Dendrogram; Determining the number of clusters in a data set; FLAME clustering; Hierarchical clustering; Information bottleneck method; Lloyd's algorithm; Nearest-neighbor chain algorithm; Neighbor joining; OPTICS algorithm; Pitman–Yor process; Single-linkage clustering; SUBCLU; Thresholding (image processing); UPGMA
Evaluation of Clustering Methods: Rand index; Dunn index; Davies–Bouldin index; Jaccard index; MinHash; K q-flats
Rule Induction: Decision rules; Rule induction; Classification rule; CN2 algorithm; Decision list; First Order Inductive Learner
Association rules and Frequent Item Sets: Association rule learning; Apriori algorithm; Contrast set learning; Affinity analysis; K-optimal pattern discovery
Ensemble Learning: Ensemble learning; Ensemble averaging; Consensus clustering; AdaBoost; Boosting; Bootstrap aggregating; BrownBoost; Cascading classifiers; Co-training; CoBoosting; Gaussian process emulator; Gradient boosting; LogitBoost; LPBoost; Mixture model; Product of Experts; Random multinomial logit; Random subspace method; Weighted Majority Algorithm; Randomized weighted majority algorithm
Graphical Models: Graphical model; State transition network
Bayesian Learning Methods: Naive Bayes classifier; Averaged one-dependence estimators; Bayesian network; Variational message passing
Markov Models: Markov model; Maximum-entropy Markov model; Hidden Markov model; Baum–Welch algorithm; Forward–backward algorithm; Hierarchical hidden Markov model; Markov logic network; Markov chain Monte Carlo; Markov random field; Conditional random field; Predictive state representation
Learning Theory: Computational learning theory; Version space; Probably approximately correct learning; Vapnik–Chervonenkis theory; Shattering (machine learning); VC dimension; Minimum description length; Bondy's theorem; Inferential theory of learning; Rademacher complexity; Teaching dimension; Subclass reachability; Sample exclusion dimension; Unique negative dimension; Uniform convergence (combinatorics); Witness set
Support Vector Machines: Kernel methods; Support vector machine; Structural risk minimization; Empirical risk minimization; Kernel trick; Least squares support vector machine; Relevance vector machine; Sequential minimal optimization; Structured SVM
Regression analysis: Outline of regression analysis; Regression analysis; Dependent and independent variables; Linear model; Linear regression; Least squares; Linear least squares (mathematics); Local regression; Additive model; Antecedent variable; Autocorrelation; Backfitting algorithm; Bayesian linear regression; Bayesian multivariate linear regression; Binomial regression; Canonical analysis; Censored regression model; Coefficient of determination; Comparison of general and generalized linear models; Compressed sensing; Conditional change model; Controlling for a variable; Cross-sectional regression; Curve fitting; Deming regression; Design matrix; Difference in differences; Dummy variable (statistics); Errors and residuals in statistics; Errors-in-variables models; Explained sum of squares; Explained variation; First-hitting-time model; Fixed effects model; Fraction of variance unexplained; Frisch–Waugh–Lovell theorem; General linear model; Generalized additive model; Generalized additive model for location, scale and shape; Generalized estimating equation; Generalized least squares; Generalized linear array model; Generalized linear mixed model; Generalized linear model; Growth curve; Guess value; Hat matrix; Heckman correction; Heteroscedasticity-consistent standard errors; Hosmer–Lemeshow test; Instrumental variable; Interaction (statistics); Isotonic regression; Iteratively reweighted least squares; Kitchen sink regression; Lack-of-fit sum of squares; Leverage (statistics); Limited dependent variable; Linear probability model; Mallows's C_p; Mean and predicted response; Mixed model; Moderation (statistics); Moving least squares; Multicollinearity; Multiple correlation; Multivariate probit; Multivariate adaptive regression splines; Newey–West estimator; Non-linear least squares; Nonlinear regression
Logistic Regression: Logit; Multinomial logit; Logistic regression
Bio-inspired Methods: Bio-inspired computing
Evolutionary Algorithms: Evolvability (computer science); Evolutionary computation; Evolutionary algorithm; Genetic algorithm; Chromosome (genetic algorithm); Crossover (genetic algorithm); Fitness function; Evolutionary data mining; Genetic programming; Learnable Evolution Model
Neural Networks: Neural network; Artificial neural network; Artificial neuron; Types of artificial neural networks; Perceptron; Multilayer perceptron; Activation function; Self-organizing map; Attractor network; ADALINE; Adaptive Neuro Fuzzy Inference System; Adaptive resonance theory; IPO underpricing algorithm; ALOPEX; Artificial Intelligence System; Autoassociative memory; Autoencoder; Backpropagation; Bcpnn; Bidirectional associative memory; Biological neural network; Boltzmann machine; Restricted Boltzmann machine; Cellular neural network; Cerebellar Model Articulation Controller; Committee machine; Competitive learning; Compositional pattern-producing network; Computational cybernetics; Computational neurogenetic modeling; Confabulation (neural networks); Cortical column; Counterpropagation network; Cover's theorem; Cultured neuronal network; Dehaene-Changeux Model; Delta rule; Early stopping; Echo state network; The Emotion Machine; Evolutionary Acquisition of Neural Topologies; Extension neural network; Feed-forward; Feedforward neural network; Generalized Hebbian Algorithm; Generative topographic map; Group method of data handling; Growing self-organizing map; Memory-prediction framework; Helmholtz machine; Hierarchical temporal memory; Hopfield network; Hybrid neural network; HyperNEAT; Infomax; Instantaneously trained neural networks; Interactive Activation and Competition; Leabra; Learning Vector Quantization; Lernmatrix; Linde–Buzo–Gray algorithm; Liquid state machine; Long short term memory; Madaline; Modular neural networks; MoneyBee; Neocognitron; Nervous system network models; NETtalk (artificial neural network); Neural backpropagation; Neural coding; Neural cryptography; Neural decoding; Neural gas; Neural Information Processing Systems; Neural modeling fields; Neural oscillation; Neurally controlled animat; Neuroevolution of augmenting topologies; Neuroplasticity; Ni1000; Nonspiking neurons; Nonsynaptic plasticity; Oja's rule; Optical neural network; Phase-of-firing code; Promoter based genetic algorithm; Pulse-coupled networks; Quantum neural network; Radial basis function; Radial basis function network; Random neural network; Recurrent neural network; Reentry (neural circuitry); Reservoir computing; Rprop; Semantic neural network; Sigmoid function; SNARC; Softmax activation function; Spiking neural network; Stochastic neural network; Synaptic plasticity; Synaptic weight; Tensor product network; Time delay neural network; U-Matrix; Universal approximation theorem; Winner-take-all; Winnow (algorithm)
Reinforcement learning: Reinforcement learning; Markov decision process; Bellman equation; Q-learning; Temporal difference learning; SARSA; Multi-armed bandit; Apprenticeship learning; Predictive learning
Text Mining: Text mining; Natural language processing; Document classification; Bag of words model; N-gram; Part-of-speech tagging; Sentiment analysis; Information extraction; Topic model; Concept mining; Semantic analysis (machine learning); Automatic summarization; String kernel; Biomedical text mining; Never-Ending Language Learning
Structure Mining: Structure mining; Structured learning; Structured prediction; Sequence mining; Sequence labeling; Process mining
Advanced Learning Tasks: Multi-label classification; Classifier chains; Web mining; Anomaly detection; Anomaly Detection at Multiple Scales; Local outlier factor; Novelty detection; GSP Algorithm; Optimal matching; Record linkage; Meta learning (computer science); Learning automata; Learning to rank; Multiple-instance learning; Statistical relational learning; Relational classification; Data stream mining; Alpha algorithm; Syntactic pattern recognition; Multispectral pattern recognition; Algorithmic learning theory; Deep learning; Bongard problem; Learning with errors; Parity learning; Inductive transfer; Granular computing; Conceptual clustering; Formal concept analysis; Biclustering; Information visualization; Co-occurrence networks
Applications: Problem domain; Recommender system; Collaborative filtering; Profiling (information science); Speech recognition; Stock forecast; Activity recognition; Data Analysis Techniques for Fraud Detection; Molecule mining; Behavioral targeting; Proactive Discovery of Insider Threats Using Graph Analysis and Learning; Robot learning; Computer vision; Facial recognition system; Outlier detection; Anomaly detection; Novelty detection