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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.


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Proceedings Article
29 Apr 2013
TL;DR: A formalism of localization for online learning problems, which, similarly to statistical learning theory, can be used to obtain fast rates, is introduced and a novel upper bound on regret in terms of classical Rademacher complexity is established.
Abstract: We introduce a formalism of localization for online learning problems, which, similarly to statistical learning theory, can be used to obtain fast rates. In particular, we introduce local sequential Rademacher complexities and other local measures. Based on the idea of relaxations for deriving algorithms, we provide a template method that takes advantage of localization. Furthermore, we build a general adaptive method that can take advantage of the suboptimality of the observed sequence. We illustrate the utility of the introduced concepts on several problems. Among them is a novel upper bound on regret in terms of classical Rademacher complexity when the data are i.i.d.

8 citations

Proceedings ArticleDOI
17 Oct 2008
TL;DR: The research results show that the prediction accuracy of SVM-Regression is better than that of neural network.
Abstract: Construction project cost forecasting is a key procedure to the mangement project. An accurate forecast can support the investment decision and ensure the project's feasible at the minimal cost. So reasonable determining and controlling the project cost become the most important task in the budget management of the construction project. A novel regression technique, called Support Vector Machines (SVM), based on the statistical learning theory is exploded in this paper for the prediction of construction project cost. SVM is based on the principle of Structure Risk Minimization as opposed to the principle of Empirical Risk Minimization supported by conventional regression techniques. Through introduced the theory of the SVM-Regression, considered and extracted substances components of construction project as parameters, this paper seted up the Model of the Construction Project Cost Forecasting based on the SVM. The research results show that the prediction accuracy of SVM-Regression is better than that of neural network.

8 citations

Proceedings ArticleDOI
22 May 2005
TL;DR: This tutorial uses an intuitive geometric examination of SVM classification to introduce SVMs and understand the principals behind them, and provides intuitions behind the extensive statistical learning theory underlying the approach without going into mathematical detail.
Abstract: Support Vector Machines (SVMs) and other Kernel Methods have emerged as a predominant family of methods in machine learning in the last ten years. The goal of this tutorial is to provide a basic understanding of the ideas behind kernel methods sufficient to allow practical application of the methods and to provide background for those interested in researching further into the subject. First we will use an intuitive geometric examination of SVM classification to introduce SVMs and understand the principals behind them. We will provide intuitions behind the extensive statistical learning theory underlying the approach without going into mathematical detail. Then, we will use case studies in regression and principal component analysis to show how these basic principals can be applied to make practical robust nonlinear versions of other linear inference methods. The power and flexibility of kernel methods comes from their ability to easily use different kernel functions. We will investigate how by changing kernel functions, the same kernel method can be applied to both traditional vector data as well as non-vector data such as strings and graphs. We will then examine some of the practical issues in support vector machines such as parameter selection, algorithms, and software. We will conclude with a discussion of the strengths and limitations of kernel methods. Outline: I. What are Support Vector Machine and Kernel Methods? II. Intuitive guide to SVM Classification a. Linear classification b. Capacity control c. Nonlinear classification III. Case studies a. Regression b. PCA

8 citations

Journal ArticleDOI
TL;DR: In this paper, a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements is described.
Abstract: This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the ...

8 citations

Proceedings ArticleDOI
Lei Wang1, Guizhi Xu1, Lei Guo1, Xuena Liu1, Shuo Yang1 
22 Oct 2007
TL;DR: OCSVM, which tries to find the smallest hypersphere enclosing target data in high dimensional space by kernel function, is firstly explored into the application to 3D reconstruction and Immune Algorithm and K-fold cross validation are introduced to intelligently search optimal parameter.
Abstract: Due to complexity and irregulation of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image has been a hot area. Support vector machine (SVM) based on statistical learning theory is mainly utilized in classification and regression. One Class SVM (OCSVM) was originally proposed for solving some special classification problems. In this paper, OCSVM, which tries to find the smallest hypersphere enclosing target data in high dimensional space by kernel function, is firstly explored into the application to 3D reconstruction. However, selecting parameters for OCSVM is a complicated problem. In order to reduce the blindness of parameter selection and perfect SVM theory, Immune Algorithm (IA) and K-fold cross validation are introduced to intelligently search optimal parameter. The experimental results demonstrate OCSVM is effective with high reconstruction accuracy.

8 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202219
202159
202069
201972
201847