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Machine Learning

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

This tutorial provides a brief overview of a number of important tools that form the crux of the modern machine learning toolbox. These tools can be used for supervised learning, unsupervised learning, reinforcement learning and their numerous variants developed over the years. Because of the lack of space, this survey is not intended to be comprehensive. Interested readers are referred to conference proceedings such as Neural Information Processing Systems (GlossaryTerm

NIPS

) and the International Conference on Machine Learning (GlossaryTerm

ICML

) for the most recent advances.

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Abbreviations

BIC:

Bayesian information criterion

BMA:

Bayes model averaging

BMF:

binary matrix factorization

BYY:

Bayesian Yin-Yang

DCA:

de-correlated component analysis

EM:

expectation maximization

FA:

factor analysis

HMM:

hidden Markov model

HT:

Hough transform

ICA:

independent component analysis

ICML:

International Conference on Machine Learning

IFA:

independent factor analysis

KPCA:

kernel principal component analysis

LDA:

linear discriminant analysis

LFA:

local factor analysis

MCA:

minor component analysis

MDL:

minimum description length

MDP:

Markov decision process

MIL:

multi-instance learning

MIML:

multi-instance, multi-label learning

MLR:

multi-response linear regression

MSA:

minor subspace analysis

MTFL:

multi-task feature learning

MTL:

multi-task learning

NFA:

non-Gaussian factor analysis

NIPS:

neural information processing system

NMF:

nonnegative matrix factorization

PCA:

principal component analysis

PSA:

principal subspace analysis

RBF:

radial basis function

RHT:

randomized Hough transform

RMTL:

regularized multi-task learning

RPCL:

rival penalized competitive learning

S3VM:

semi-supervised support vector machine

SARSA:

state-action-reward-state-action

SBF:

subspace-based function

SSM:

state–space model

TD:

temporal difference

TFA:

temporal factor analysis

WTA:

winner-take-all

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Authors and Affiliations

  1. Dep. Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong

    James T. Kwok

  2. National Key Lab. for Novel Software Technology, Nanjing University, 210023, Nanjing, China

    Zhi-Hua Zhou

  3. Dep. Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Hong Kong

    Lei Xu

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  1. James T. Kwok
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  2. Zhi-Hua Zhou
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  3. Lei Xu
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Correspondence to James T. Kwok .

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  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Kwok, J.T., Zhou, ZH., Xu, L. (2015). Machine Learning. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_29

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

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