User:Eric.chereau/Books/Machine Learning 2.2

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
 * Bayesian additive regression kernels
 * 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 Cp
 * 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