Journal Article10.1016/J.PATREC.2008.02.022
Bayes Machines for binary classification
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TL;DR: Experimental results indicate that the proposed approach to binary classification based on an extension of Bayes Point Machines outperforms Support Vector Machines over several of the classification problems studied and is competitive with other Bayesian classification algorithms based on Gaussian Processes.
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About: This article is published in Pattern Recognition Letters. The article was published on 10 Jul 2008. The article focuses on the topics: Bayesian linear regression & Bayesian inference.
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References
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Chih-Chung Chang,Chih-Jen Lin +1 more
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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Individual Comparisons by Ranking Methods
TL;DR: The comparison of two treatments generally falls into one of the following two categories: (a) a number of replications for each of the two treatments, which are unpaired, or (b) we may have a series of paired comparisons, some of which may be positive and some negative as mentioned in this paper.
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