Machine learning : an algorithmic perspective /
Marsland, Stephen.
Machine learning : an algorithmic perspective / Stephen Marsland. - 2nd. ed. - xx, 437 pages : illustrations ; 25 cm. - Chapman & Hall/CRC machine learning & pattern recognition series .
"A Chapman & Hall book."
Includes bibliographical references and index.
Introduction -- Preliminaries -- Neurons, neural networks, and linear discriminants -- The multi-layer perceptron -- Radial basis functions and splines -- Dimensionality reduction -- Probabilistic learning -- Support vector machines -- Optimisation and search -- Evolutionary learning -- Reinforcement learning -- Learning with trees -- Decision by committee: ensemble learning -- Unsupervised learning -- Markov chain Monte Carlo (MCMC) methods -- Graphical models -- Symmetric weights and deep belief networks -- Gaussian processes -- Python.
Annotation
Written in an easily accessible style, this text provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization.
9781466583283 (hbk) 1466583282 (hbk)
2014434325
Machine learning.
Algorithms.
Q325.5 / .M368 2015
006.31 / M.S.M
Machine learning : an algorithmic perspective / Stephen Marsland. - 2nd. ed. - xx, 437 pages : illustrations ; 25 cm. - Chapman & Hall/CRC machine learning & pattern recognition series .
"A Chapman & Hall book."
Includes bibliographical references and index.
Introduction -- Preliminaries -- Neurons, neural networks, and linear discriminants -- The multi-layer perceptron -- Radial basis functions and splines -- Dimensionality reduction -- Probabilistic learning -- Support vector machines -- Optimisation and search -- Evolutionary learning -- Reinforcement learning -- Learning with trees -- Decision by committee: ensemble learning -- Unsupervised learning -- Markov chain Monte Carlo (MCMC) methods -- Graphical models -- Symmetric weights and deep belief networks -- Gaussian processes -- Python.
Annotation
Written in an easily accessible style, this text provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization.
9781466583283 (hbk) 1466583282 (hbk)
2014434325
Machine learning.
Algorithms.
Q325.5 / .M368 2015
006.31 / M.S.M