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Machine Learning

Machine Learning

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  • ISBN:9781420067187
  • Editorial: Taylor & Francis
  • Fecha de la edición:
  • Encuadernación:
  • Nº Pág.:406
  • Idiomas:Español

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"Machine Learning: An Algorithmic Perspective" introduces this subject to computer science students and others who may not have a strong mathematical background. Focusing on algorithms and applications, the text presents three distinct sets of problems for each section: standard questions that test understanding of the material, structured programming exercises using code and data from the Internet, and suggested further investigations, often involving some basic programming. The book covers such fundamental topics as neuronal modeling, perceptron, multi-layer perceptron, classification, regression, decision trees, the naive Bayes' classifier, unsupervised learning, the self-organizing map, and genetic algorithms.

1 Introduction 1
2 Linear discriminants 17
3 The multi-layer perceptron 47
4 Radial basis functions and splines 95
5 Support vector machines 119
6 Learning with trees 133
7 Decision by committee : ensemble learning 153
8 Probability and learning 167
9 Unsupervised learning 195
10 Dimensionality reduction 221
11 Optimisation and search 247
12 Evolutionary learning 269
13 Reinforcement learning 293
14 Markov chain Monte Carlo (MCMC) methods 315
15 Graphical models 333
16 Python 365
Index 383

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