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Machine Learning, Neural And Statistical Classification

Machine Learning, Neural And Statistical Classification

Machine Learning, Neural And Statistical Classification:

Author: Alpaydin Ethem 

Publisher: PHI Learning Pvt. Ltd

Language: English

Pages: 640 pages

About The Author:

Machine Learning, Neural And Statistical Classification – Donald Mikki was a British researcher in artificial intelligence. During World War II, Mikkie worked for the Government Code and Cypher School at Bletchley Park, contributing to the effort to solve “Tunny”, a German teleprinter.

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About This Book:

The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. Before describing the contents, we first need to define what we mean by classification, give some background to the different perspectives on the task, and introduce the European Community StatLog project whose results form the basis for this book.

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  • 1.   Introduction
    2    Supervised learning
    3.   Bayesian decision theory
    4.   Parametric methods
    5.   Multivariate methods
    6.   Dimensionality reduction
    7.   Clustering
    8.   Nonparametric methods
    9.   Decision trees
    10. Linear discrimination
    11. Multiplayer perceptrons
    12. Local models;
    13. Kernel machines;
    14. Graphical models
    15. Hidden Markov models
    16. Bayesian estimation;
    17. Combining multiple learners;
    18. Reinforcement learning;
    19. Design and analysis of machine learning experiments;
    a. Probability

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