Journal ArticleDOI
The theoretical foundations of statistical learning theory based on fuzzy number samples
Ming-Hu Ha,Jing Tian +1 more
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TLDR
The concepts of fuzzy expected risk functional, fuzzy empiricalrisk functional and fuzzy empirical risk minimization principle are redefined and the key theorem of learning theory based on fuzzy number samples is proved.About:
This article is published in Information Sciences.The article was published on 2008-08-01. It has received 13 citations till now. The article focuses on the topics: Statistical learning theory & Algorithmic learning theory.read more
Citations
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Proceedings ArticleDOI
A Research on Extracting Low Quality Human Finger Vein Pattern Characteristics
TL;DR: An enhanced method for extracting finger vein features based on fuzzy theory and combined the image segmentation algorithm and concave detection algorithm is proposed and shows that it is not only effective in removing noise and false vein feature, but also extracted vein features is smooth and continuous.
Journal ArticleDOI
A cognitive interactionist sentence parser with simple recurrent networks
Yi Guo,Zhiqing Shao,Nan Hua +2 more
TL;DR: A novel and effective sentence parser, the Cognitive Interactionist Parser (CIParser), which incorporates the cognitive interactionist approach with semantic information and simple recurrent networks to extend and enrich the technologies for sentence parsing.
Journal Article
Structural Risk Minimization Principle Based on Complex Random Samples
TL;DR: This paper explores statistical learning theory based on complex fuzzy random samples and the idea of the complex fuzzy structural risk minimization principle is presented and the bound on the asymptotic rate of convergence is derived.
Book ChapterDOI
The key theorem of learning theory based on Sugeno measure and fuzzy random samples
TL;DR: The key theorem of learning theory based on Sugeno measure and fuzzy random samples is proved, which will play an important role in the systematic and comprehensive development of the Statistical Learning Theory based on Skewed random samples.
Proceedings ArticleDOI
A Further Discussion on Bounds on the Rate of Uniform Convergence of Learning Theory Based on Fuzzy Samples
Jiqiang Chen,Minghu Ha +1 more
TL;DR: This study proves a certain law of Hoeffding inequality for fuzzy random variables and presents the bounds on the rate of uniform convergence of learning theory based on fuzzy samples in probability space, which become cornerstones of the theoretical fundamentals of the SLT for fuzzy samples.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
The Nature of Statistical Learning Theory
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?
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.
Journal ArticleDOI
The concept of a linguistic variable and its application to approximate reasoning—II☆
TL;DR: Much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior.
Journal ArticleDOI
An overview of statistical learning theory
TL;DR: How the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms are demonstrated and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems are demonstrated.