New Support Vector Algorithms
Reads0
Chats0
TLDR
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.Abstract:
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.read more
Citations
More filters
Journal ArticleDOI
Age Estimation via Grouping and Decision Fusion
TL;DR: The GEF system outperforms the existing state-of-the-art age estimation methods by a significant margin, and the mean absolute errors of age estimation are reduced from 4.48 to 2.81 years on FG-NET and 3.97 years on MORPH-II.
Journal ArticleDOI
Kernel-based learning and feature selection analysis for cancer diagnosis
TL;DR: Experimental results demonstrate that the proposed complete cancer diagnostic process through kernel-based learning and feature selection is efficient and provides a higher classification accuracy rate using a reduced number of genes.
Journal ArticleDOI
Application of support vector machines for fault diagnosis in power transmission system
TL;DR: In this paper, support vector machines (SVM) are used as Intelligence tool to identify the faulted line that is emanating and finding the distance from the substation, which may help to improve the fault monitoring/diagnosis process, thus assuring secure operation of the power systems.
Journal ArticleDOI
Improved v -Support vector regression model based on variable selection and brain storm optimization for stock price forecasting
TL;DR: Numerical results indicate that the developed hybrid model is not only simple but also able to satisfactorily approximate the actual CSI300stock price index, and it can be an effective tool in stock market mining and analysis.
Journal ArticleDOI
A rough margin based support vector machine
Junhua Zhang,Yuanyuan Wang +1 more
TL;DR: By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Book
Matrix Analysis
Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.