Journal ArticleDOI
Regularization and statistical learning theory for data analysis
TLDR
Techniques, like support vector machines and regularization networks, which can be justified in this theoretical framework and proved to be useful in a number of image analysis applications are discussed.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2002-02-28. It has received 75 citations till now. The article focuses on the topics: Statistical learning theory & Regularization perspectives on support vector machines.read more
Citations
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Journal ArticleDOI
Machine learning in manufacturing: advantages, challenges, and applications
TL;DR: In this article, the authors present an overview of available machine learning techniques and structuring this rather complicated area, and a special focus is laid on the potential benefit and examples of successful applications in a manufacturing environment.
Book
Business Intelligence: Data Mining and Optimization for Decision Making
TL;DR: Students following data analysis and data mining courses looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.
Journal ArticleDOI
Soft sensing modeling based on support vector machine and Bayesian model selection
TL;DR: Support vector machine (SVM), a new powerful machine learning method based on statistical learning theory (SLT), is introduced into soft sensor modeling and a model selection method within the Bayesian evidence framework is proposed to select an optimal model for a soft sensor based on SVM.
Journal ArticleDOI
Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer
TL;DR: In this article, a kernel-based regularization by support vector machines regression (SVR) method was proposed to regularize the inverse problem of a 1D canopy reflectance model.
Journal ArticleDOI
Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad
TL;DR: In this paper, an improved support vector machine (SVM) model was proposed to forecast the weekly generated waste of Mashhad city using the principal component analysis (PCA) technique.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book ChapterDOI
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
TL;DR: This chapter reproduces the English translation by B. Seckler of the paper by Vapnik and Chervonenkis in which they gave proofs for the innovative results they had obtained in a draft form in July 1966 and announced in 1968 in their note in Soviet Mathematics Doklady.
Book
A Probabilistic Theory of Pattern Recognition
TL;DR: The Bayes Error and Vapnik-Chervonenkis theory are applied as guide for empirical classifier selection on the basis of explicit specification and explicit enforcement of the maximum likelihood principle.