User:Dylanewheeler/Books/Wikipattern

Intro to Pattern Recognition and its Algorithms

 * Intro
 * Pattern recognition
 * Feature selection
 * Data mining
 * Probabilistic classification


 * Classification Algorithms
 * Statistical classification
 * Linear discriminant analysis
 * Quadratic classifier
 * Multinomial logistic regression
 * Variable kernel density estimation
 * Decision tree
 * K-nearest neighbors algorithm
 * Naive Bayes classifier
 * Artificial neural network
 * Perceptron
 * Support vector machine


 * Clustering Algorithms
 * Cluster analysis
 * Deep learning
 * Hierarchical clustering
 * K-means clustering
 * Correlation clustering
 * Kernel principal component analysis


 * Ensemble Learning Algorithms
 * Ensemble learning
 * Boosting (machine learning)
 * Bootstrap aggregating
 * Ensemble averaging (machine learning)


 * General Algorithms
 * Bayesian network
 * Markov random field


 * Multilinear Subspace Learning Algorithms
 * Multilinear subspace learning
 * Multilinear principal component analysis


 * Real-Valued Sequence Labeling Algorithms
 * Sequence labeling
 * Kalman filter
 * Particle filter


 * Regression Algorithms
 * Regression analysis
 * Kriging
 * Linear regression
 * Independent component analysis
 * Principal component analysis


 * Sequence Labeling Algorithms
 * Conditional random field
 * Hidden Markov model
 * Maximum-entropy Markov model
 * Recurrent neural network


 * Datasets
 * List of datasets for machine learning research