About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 254. Chapters: Artificial neural network, Supervised learning, Hidden Markov model, Pattern recognition, Reinforcement learning, Principal component analysis, Self-organizing map, Overfitting, Cluster analysis, Granular computing, Rough set, Mixture model, Expectation-maximization algorithm, Radial basis function network, Types of artificial neural networks, Learning to rank, Forward-backward algorithm, Perceptron, Category utility, Neural modeling fields, Dominance-based rough set approach, Principle of maximum entropy, Non-negative matrix factorization, Concept learning, K-means clustering, Structure mapping engine, Viterbi algorithm, Cross-validation, Hierarchical temporal memory, Activity recognition, Algorithmic inference, Formal concept analysis, Gradient boosting, Information bottleneck method, Nearest neighbor search, Simultaneous localization and mapping, Markov decision process, Gittins index, K-nearest neighbor algorithm, General Architecture for Text Engineering, Reasoning system, Concept drift, Uniform convergence, Conceptual clustering, Multi-armed bandit, Multilinear subspace learning, Conditional random field, DBSCAN, Feature selection, Learning with errors, Weka, Evolutionary algorithm, Iris flower data set, Binary classification, OPTICS algorithm, Partially observable Markov decision process, Constrained Conditional Models, Group method of data handling, Learning classifier system, Random forest, Statistical classification, Analogical modeling, Bregman divergence, Backpropagation, Temporal difference learning, Loss function, Curse of dimensionality, Alternating decision tree, Evolutionary multi-modal optimization, Stochastic gradient descent, Kernel principal component analysis, Explanation-based learning, K-medoids, RapidMiner, Transduction, Variable-order Markov model, Kernel adaptive filter, Classification...
