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.

Concentration inequalities for regenerative and Harris recurrent Markov chains with applications to statistical learning.

Abstract: Concentration inequalities are very often a crucial step in deriving many results in statistical learning. The purpose of this talk is to present Bernstein and Hoeffding type maximal inequalities for regenerative Markov chains. Furthermore, we generalize these results and show exponential bounds for suprema of empirical processes over a class of functions F which size is controlled by its uniform entropy number. We show also that concentration inequalities are possible to obtain when the chain is sub-geometric. All constants involved in the bounds of the considered inequalities are given in an explicit form which can be advantageous in practical considerations. We show that the inequalities obtained for regenerative Markov chains can be easily generalized to a Harris recurrent case. Finally we provide one example of application of presented inequalities in statistical learning theory and obtain generalization bounds for mimimum volume set estimation problem when the data are Markovian.

Research on Electric-Power Load Forecasting Based on Support Vector Machine Regression Technique

Abstract: Based on the Statistical Learning Theory,a novel regression technique called Support Vector Machine(SVM),is explored in this paper for electric-power load forecasting.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.

Application of SVM Combined with Mackov Chain for Inventory Prediction in Supply Chain

Abstract: The aim of this paper is to predict the inventory of the relevant upstream enterprises in supply chain. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a short-term stage forecasting model. However, take the fact into account that demand signal is affected by variant random factors and behaves big uncertainty, the predicted accuracy of SVM is not approving when the data show great randomness. It is obligatory that we present Markov chain to improve the predicted accuracy of SVM. This combined model takes advantage of the high predictable power of SVM model and at the same time take advantage of the prediction power of Markov chain modeling on the discrete states based on the SVM modeling residual sequence. Then we use the statistical data of the output of the gasoline of China from Feb-06 to Dec-07 for a validation of the effectiveness of the above model.

Trainable Visual Models for Object Class Recognition.

Abstract: Recognizing object classes, such as cars, planes or elephants, in an image or a video remains one of the most challenging problems in Computer Vision. However, recently a number of successes have been achieved by using ideas and algorithms from statistical learning theory, where visual models are trained using positive and negative examples of the class.