User:Sooshie/Books/Statistical Learning

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Statistical Learning

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
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
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
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)