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.
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Journal Article•
TL;DR: A novel regression technique, called support vector machine (SVM), based on the statistical learning theory is applied in this paper for the prediction of natural gas demands and shows that the prediction accuracy of SVM is better than that of neural network.
Abstract: Machine learning techniques are finding more and more applications in the field of load forecasting A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied in this paper for the prediction of natural gas demands Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques The prediction result shows that the prediction accuracy of SVM is better than that of neural network Thus,SVM appears to be a very promising prediction tool The software package NGPSLF based on SVM prediction has been put into practical business application
8 citations
Posted Content•
TL;DR: If the bound on the accuracy is taken into account, quantum machine learning algorithms cannot achieve polylogarithmic runtimes in the input dimension, calling for a careful revaluation of quantum speedups for learning problems, even in cases where quantum access to the data is naturally available.
Abstract: Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the bound on the accuracy is taken into account, quantum machine learning algorithms -- for which statistical guarantees are available -- cannot achieve polylogarithmic runtimes in the input dimension. This calls for a careful revaluation of quantum speedups for learning problems, even in cases where quantum access to the data is naturally available.
8 citations
15 Oct 2001
TL;DR: This work investigates the ability of SVM to perform appearance-based object recognition and initial experiments indicated that this may be a promising approach to the problem.
Abstract: Support vector machines (SVM) are a class of algorithms derived from the statistical learning theory that are receiving growing interest by the computer vision community as they present some advantages over classical techniques. This work investigates the ability of SVM to perform appearance-based object recognition. Initial experiments indicated that this may be a promising approach to the problem.
8 citations
TL;DR: This paper uses tools of statistical learning in order to design a more accurate prediction operator in Harten's framework based on a training sample, resulting in multiresolution decompositions with enhanced sparsity.
8 citations
01 Jan 2003
TL;DR: In this paper, the authors presented a method of model construction for the power system transient stability assessment based on statistical learning theory integrated with the bagging and the approximate reasoning, which takes full advantage of its ability to solve the problem with small sample, nonlinear and high dimension.
Abstract: This paper presents a method of model construction for the power system transient stability assessment based on statistical learning theory integrated with the bagging and the approximate reasoning. Support vector machines (SVM) operate on the principle of structure risk minimization. This paper takes full advantage of its ability to solve the problem with small sample, nonlinear and high dimension. Hence better generalization ability is guaranteed. The multi-class identification for power system transient stability assessment is solved by the data set reconstruction. The assessment model uses the data set regulation, bagging and approximate reasoning to improve the training speed, the accuracy and stability of the estimation result. The IEEE 39-Bus test system is employed to demonstrate the validity of the proposed approach.
8 citations