scispace - formally typeset
Search or ask a question
Topic

Statistical learning theory

About: Statistical learning theory is a research topic. Over the lifetime, 1618 publications have been published within this topic receiving 158033 citations.


Papers
More filters
Proceedings ArticleDOI
13 Nov 2006
TL;DR: varepsilon -SVRVGA, a detailed implementation of SVR that uses the non-traditional Vasconcelos Genetic Algorithm (VGA) as tool for solving the associated QP along with the tuning of the kernel parameters, is discussed.
Abstract: Support Vector Machines (SVM) are learning methods useful for solving supervised learning problems such as classification (SVC) and regression (SVR). SVM's are based on the Statistical Learning Theory and the minimization of the Structural Risk [1], an enhancement over neural networks such as Multi-Layer Perceptrons. However, the major drawback is the high computational cost of the constrained Quadratic Problem (QP) combined with the selection of the kernel parameters they involve. Here we discuss \varepsilon -SVRVGA, a detailed implementation of SVR that uses the non-traditional Vasconcelos Genetic Algorithm (VGA) [2] as tool for solving the associated QP along with the tuning of the kernel parameters. This work does not explore the automatic tuning of the regularization parameter C associated to the VC dimension [1] of the SVM what is considered an open research area. The \varepsilon -SVRVGA fitting capability was tested with onedimensional Time Series (TS) data by reconstructing their n-dimensional state space [3] and adding Gaussian noise. Results show that \varepsilon -SVRGVA is able to model successfully the TS in spite of a noisy environment as well as the self-selection of kernel parameters.

4 citations

Journal ArticleDOI
Pijush Samui1
TL;DR: A sensitivity analysis of SVM parameters (σ, 𝐶, and e) has been presented and it is shown that the SVM is a robust tool for site characterization.
Abstract: The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values (𝑁) spread over 220 sq km area of Bangalore. The model is applied for corrected 𝑁 (𝑁𝑐) values. The three input variables (𝑥, 𝑦, and 𝑧, where 𝑥, 𝑦, and 𝑧 are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the 𝑁𝑐 data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, 𝐶, and e) has been also presented.

4 citations

Journal ArticleDOI
01 Jan 2016
TL;DR: The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well and it will provide a new method to predict foundation settlement.
Abstract: The suppor 1 t vector machine (SVM) is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

4 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a book which attempts to explain the theory behind machine learning, which is always welcome, as machine learning is such a rapidly developing and evolving field, any book which attempt to explain its theory behind it is always useful.
Abstract: Machine learning is such a rapidly developing and evolving field, any book which attempts to explain the theory behind it is always welcome.In the subtitle the statistical learning theory is the ke...

4 citations

01 Jan 2012
TL;DR: This document provides a general description of Support Vector Machine technique, a technique based on the statistical learning theory, and a survey of the many applications of the algorithm in the bioinformatics domain.
Abstract: Many scientists and researchers have been considering Support Vector Machines (SVMs) as one of the most powerful and robust algorithm in machine learning. For this reason, they have been used in many fields, such as pattern recognition, image processing, robotics, and many others. Since their appearance in 1995, from an idea of Vladimir Vapnik, bioinformatics community started to use this new technique to solve the most common classification and clustering problems in the biomolecular domain. In this document, we first give a general description of Support Vector Machine technique, a technique based on the statistical learning theory (Section 1). Then we provide a survey of the many applications of the algorithm in the bioinformatics domain (Section 2). Finally, we report a short list of SVM implementation codes available on the internet (Section 3). About this survey This document is freely available and can be download from http://www.DavideChicco.it author’s website. Alessandro Lazaric (INRIA, Lille, France, EU) kindly supervised and corrected this document before publication.

4 citations


Network Information
Related Topics (5)
Artificial neural network
207K papers, 4.5M citations
86% related
Cluster analysis
146.5K papers, 2.9M citations
82% related
Feature extraction
111.8K papers, 2.1M citations
81% related
Optimization problem
96.4K papers, 2.1M citations
80% related
Fuzzy logic
151.2K papers, 2.3M citations
79% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202219
202159
202069
201972
201847