Power System On-Line Static Security Assessment by Using Multi-Class Support Vector Machines

Multi-class Support Vector Machines

Support Vector Machines for Multi-class Classification

A b s t r a c t : Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can be used to solve a K-class classification problem, such a procedure requires some care. In this paper, the scaling problem of different SVMs is highlighted. Various normalization methods are proposed to cope wi...

Feature Selection for Multi-class Problems Using Support Vector Machines

Since feature selection can remove the irrelevant features and improve the performance of learning systems, it is an crucial step in machine learning. The feature selection methods using support vector machines have obtained satisfactory results, but the previous works are usually for binary classification, and needs auxiliary techniques to be extended to multiple classification. In this paper,...

On-Line Laplacian One-Class Support Vector Machines

We propose a manifold regularization algorithm designed to work in an on–line scenario where data arrive continuously over time and it is not feasible to completely store the data stream for training the classifier in batch mode. The On–line Laplacian One–Class SVM (OLapOCSVM) algorithm exploits both positively labeled and totally unlabeled examples, updating the classifier hypothesis as new da...

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P