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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.


Papers
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Journal ArticleDOI
TL;DR: It is demonstrated that trained SVMs with a radial basis function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

63 citations

Journal ArticleDOI
TL;DR: In this paper, support vector machines (SVMs) based on statistical learning theory (SLT) and the principles of structural risk minimization (SRM) and empirical risk minimisation (ERM) use an analytical approach to classification and regression.

63 citations

Proceedings ArticleDOI
07 Aug 2002
TL;DR: The experimental comparison between the support vector machine and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series.
Abstract: Forecasting of air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by airborne pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and has been reported to perform well with some promising results. The work presented examines the feasibility of applying SVM to predict pollutant concentrations. The functional characteristics of the SVM are also investigated. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series.

62 citations

Journal ArticleDOI
TL;DR: The most recent advances in supervised machine learning and highdimensional models for time series forecasting are surveyed and ensemble and hybrid models by combining ingredients from different alternatives are considered.
Abstract: In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.

62 citations

Journal ArticleDOI
TL;DR: The experimental results show that finding the splitting hyperplane is not a trivial task and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space and the utility of GSVM -AR is very good because it is easy to be implemented.

62 citations


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