User:Pfcohen/Books/Machine Learning

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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
Features Selection and Features Extraction: Data Pre-processing; Discretization of continuous features; 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
ToC