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Classification

Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray

in Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Published in print:
2014
Published Online:
October 2017
ISBN:
9780691151687
eISBN:
9781400848911
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691151687.003.0009
Subject:
Physics, Particle Physics / Astrophysics / Cosmology

Chapter 6 described techniques for estimating joint probability distributions from multivariate data sets and for identifying the inherent clustering within the properties of sources. This approach ... More


Machine learning with sklearn

Thomas P. Trappenberg

in Fundamentals of Machine Learning

Published in print:
2019
Published Online:
January 2020
ISBN:
9780198828044
eISBN:
9780191883873
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198828044.003.0003
Subject:
Neuroscience, Behavioral Neuroscience

This chapter’s goal is to show how to apply machine learning algorithms in a general setting using some classic methods. In particular, it demonstrates how to apply three important machine learning ... More


Statistical machine learning

Max A. Little

in Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics

Published in print:
2019
Published Online:
October 2019
ISBN:
9780198714934
eISBN:
9780191879180
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198714934.003.0006
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy, Mathematical Physics

This chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way ... More


Transductive Support Vector Machines

Joachims Thorsten

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0006
Subject:
Computer Science, Machine Learning

This chapter discusses the transductive learning setting proposed by Vapnik where predictions are made only at a fixed number of known test points. Transductive support vector machines (TSVMs) ... More


Semi-Supervised Learning Using Semi-Definite Programming

De Bie Tijl and Cristianini Nello

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0007
Subject:
Computer Science, Machine Learning

This chapter discusses an alternative approach that is based on a convex relaxation of the optimization problem associated with support vector machine transduction. The result is a semi-definite ... More


Function Approximation

Masashi Sugiyama and Motoaki Kawanabe

in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.003.0002
Subject:
Computer Science, Machine Learning

This chapter discusses function learning methods under covariate shift. Ordinary empirical risk minimization learning is not consistent under covariate shift for misspecified models, and this ... More


Semi-Supervised Protein Classification Using Cluster Kernels

Weston Jason, Leslie Christina, Ie Eugene, and Noble William Stafford

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0019
Subject:
Computer Science, Machine Learning

This chapter describes an experimental study of large-scale semi-supervised learning for the problem of protein classification. The protein classification problem, a central problem in computational ... More


Advanced Topics

Jeffrey S. Racine

in Reproducible Econometrics Using R

Published in print:
2019
Published Online:
January 2019
ISBN:
9780190900663
eISBN:
9780190933647
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780190900663.003.0007
Subject:
Economics and Finance, Econometrics

This chapter covers two advanced topics: a machine learning method (support vector machines useful for classification) and nonparametric kernel regression.


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