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.

Short-term Load Forecasting Based on Support Vector Machines Mixture Models

Abstract: In this paper,support vector machines(SVMs) expert,a mixture structure,is presented to forecast short term load in power systems.Support vector machines(SVMs) expert has a two-stage neural network architecture of SVMs,In the first stage,self-organizing feature map(SOM) is used as a clustering algorithm to partition the whole load input data into several disjointed regions,and the training data with similar features in the input space are belonged to the same region.Then,in the second stage,the multiple SVMs,also called SVM experts,that best fit to forecast short term load in the partitioned regions are constructed by finding the most appropriate kernel function of SVMs.SVMs is an advance learning algorithm which are based on statistical learning theory(SLT) and have perfect mathematical theory basement.SVMs are robust to over-fit learning problems and white noises,and have good generalized ability to solve many kinds of time series forecasting problems.Meanwhile the optimal solution of SVMs is absolute unique and optimal in whole feasible solution space,and is superior to artificial neural networks.So,in recent years,SVMs have been used to forecast short term load in power systems in many literatures.However,the short term load data change periodically,it is not appropriate using SVM for forecasting directly,So we proposes Support vector machines expert using a mixture structure in this paper.Compared to single SVMs models with polynomial kernels,Gaussian kernels and the third order spline kernels,the proposed algorithm in this paper have three advantages,i.e.more accurate forecasting,fewer support vector machines and smaller calculation complexity.Finally,using actual short-term load data of one city in south china as training data,the experiment shows that the SVMs expert's forecasting precise is enhanced 10 times and calculating complexity is descend 100 times.

The Bounds of a Kind of Regression Estimation Problem on Sugeno Measure Space

Abstract: Statistical Learning Theory is further discussed on Sugeno measure spaceThe definition of the conditional expectation of gλ random variable on Sugeno measure space is givenA model of regression estimation by series expansion using observations with additive noise on Sugeno measure space is establishedFor this model the significantly bounds are given and proven

Spectrum Sensing Algorithm Based on Twin Support Vector Machine

Abstract: Spectrum sensing is the key of implementing cognitive radio technology. As a kind of machine learning method based on statistical learning theory, support vector machine has the advantages of global optimization, nonlinearity and good generalization ability. The use of support vector machines in spectrum sensing can solve the problem that parameters in spectrum sensing are difficult to determine by learning historical data. Aiming at the problem that the training time of the spectrum perception algorithm based on support vector machine is too long, this paper proposes a spectrum sensing algorithm based on twin support vector machine based on fuzzy mathematics and twin support vector machine. The algorithm extracts the leading eigenvector as the input features that can reflect the signal correlation and calculate the membership degree according to the proportion of the maximum eigenvalue. The twin support vector machine is used to solve the classification hyperplane. The algorithm complexity is only 1/4 of the SVM algorithm, which can greatly reduce the training time. The simulation results show that under equal prior condition, when the number of users and the number of samples are the same, the spectrum sensing algorithm based on TWSVM can obtain a lower minimum error probability than the SVM-based spectrum sensing algorithm and energy detection. As the number of users and the number of sampling points increase, the minimum error probability of the TWSVM-based spectrum sensing algorithm decreases.

Statistical Learning Theory & Support Vector Machince Method

Abstract: Studying from the Statistical Learning Theory(SLT),based on the general principle of SVM,the research topics which are learning algorithms,simplification algorithms and multi-classification are discussed.In order to analysis the effects of SVM,three experiments are presented and discussed.

A Review of Using Support Vector Machine Theory to Do Stock Forecasting

Abstract: support vector machine (SVM) is developed based on statistical learning theory new method, its training algorithm is essentially a problem of solving the quadratic programming. This paper summarizes the basic principle of SVM, and then use SVM to stock prediction research status at home and abroad were reviewed, analyzed using SVM analysis, stock price, stock index also simple analysis of financial condition, finally, the existing problems and development trend in this field were discussed.