User:Venukannur/Books/ReviewPaper Reference Materials

Learning for tracking

 * Sparse matrix
 * Lp space
 * Compressed sensing
 * Multiple-instance learning
 * Boosting (machine learning)
 * AdaBoost
 * Mean shift
 * Proximal gradient methods for learning
 * K-SVD
 * Sparse approximation
 * Hough transform
 * Visual descriptors
 * Histogram of oriented gradients
 * Online machine learning
 * Random forest
 * GrabCut
 * Support vector machine
 * Kernel method
 * Sparse matrix
 * Lp space
 * Compressed sensing
 * Multiple-instance learning
 * Boosting (machine learning)
 * AdaBoost
 * Mean shift
 * Proximal gradient methods for learning
 * K-SVD
 * Sparse approximation
 * Hough transform
 * Visual descriptors
 * Histogram of oriented gradients
 * Online machine learning
 * Random forest
 * GrabCut
 * Support vector machine
 * Kernel method
 * Monte Carlo method
 * Bayesian inference
 * Particle filter
 * Nonlinear dimensionality reduction
 * Principal component analysis
 * Independent component analysis
 * Singular value decomposition
 * Factor analysis
 * K-nearest neighbors algorithm
 * RANSAC
 * Markov process
 * Markov chain
 * Dynamic Markov compression
 * Hidden Markov model
 * Hellinger distance
 * Linear regression
 * Segmented regression
 * Multi-task learning
 * Inductive transfer
 * Markov logic network
 * Bayesian network