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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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Book ChapterDOI
25 Jun 2009
TL;DR: This work deviates from the mainstream methods by proposing a mixture of feature extraction and dimensionality reduction method based on Random Projections that is followed by widely used non-linear and probabilistic learning method, Random Forests that has been used successfully for high dimensional data in various applications of Machine Learning.
Abstract: Speech recognition is among the harderst engineering problems,it has drawn the attention of various researchers over a wide range of fields. In our work we deviate from the mainstream methods by proposing a mixture of feature extraction and dimensionality reduction method based on Random Projections that is followed by widely used non-linear and probabilistic learning method,Random Forests that has been used successfully for high dimensional data in various applications of Machine Learning. The methodological strategy decouples the problem of speech recognition to 3 distinct components: a)feature extraction, b)dimensionality reduction,c)classification scheme,since tackles the problem via Statistical Learning Theory perspective enriched by the current advances of Signal Processing.

2 citations

Proceedings ArticleDOI
28 Dec 2009
TL;DR: Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT) and some perfect computing conclusions have been acquired by the proposed model.
Abstract: Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT). SVM is powerful for the problem with small samples, non linear and high dimension. A multi-class SVM classifier is applied to predict the coal and gas outburst in the paper. In this model, the dominant factors are the input vectors and the degree of outburst danger is divided into four types: heavy outburst, common outburst, outburst warning and no existing outburst. Through a special data dealing process, the multi-class SVM classifier, trained with the sampling data, identifies out the four types of coal and gas outburst states. An empirical analysis shows that some perfect computing conclusions have been acquired by the proposed model.

2 citations

Journal Article
Tan Xiao-long1
TL;DR: The extrapolating ability and predicting capability have been validated by comparing the monitoring values and predicted ones obtained by using the support vector machine prediction model built on displacement monitoring data of the slope project and good prediction precision is approved.
Abstract: Based on the statistical learning theory and the principle of the minimum structural risk,the support vector machine(SVM) method has the excellent extrapolating ability for regression prediction and good applicability to the problem on small training data.The extrapolating ability and predicting capability have been validated by comparing the monitoring values and predicted ones obtained by using the support vector machine prediction model built on displacement monitoring data of the slope project.Based on the new data obtained by generating operation on the initial monitoring data of the slope project,the predicted results are figured out with SVM model correspondingly and good prediction precision is approved as well by comparing the predicted results based on the initial data and new data.The influence of the selected training data on the prediction precision is also analyzed.The sensitivity analysis of the parameters of SVM model is made as well.Moreover,the precision of prediction is improved by using one of evolutionary algorithms,particle swarm optimization algorithms,to optimize the key parameters of SVM model.

2 citations

Journal Article
TL;DR: Support vector machine, which is based on structural risk minimization principle, has good capability in fault pattern classification of engine parameter acquisition unit, experimental result proves.
Abstract: Support vector machine(SVM),which is based on structural risk minimization principle,is now widely used in pattern recognition,function approximation and other research fields.It shows better generalization ability than traditional statistical learning theory,especially,when used to small sample.Some dimensionless parameter is selected as the eigenvalue,and support vector machine is applied to fault diagnosis in engine parameter acquisition unit.Experimental result proves that it has good capability in fault pattern classification of engine parameter acquisition unit.

2 citations

Journal Article
TL;DR: A new kind of regression SVM is proposed and its dual formulations are deduced by modifying the standard SVM formulation and a multiplicative updates learning algorithm is designed on the basis of an optimal theorem.
Abstract: Support vector machine(SVM) is a learning technique based on the structural risk minimization principle,and it is also a class of regression method with good generalization ability.By modifying the standard SVM formulation,this paper proposes a new kind of regression SVM and deduces its dual formulations.Then a multiplicative updates learning algorithm is designed on the basis of an optimal theorem.The updates algorithm converges monotonically on the extremum solution of the optimal problem and has a simple iterative form.Experimental results of simulation indicate the feasibility of the new regression SVM and its training algorithm.

2 citations


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