Complex Support Vector Machines for Regression and Quaternary Classification

Support vector machines for classification and regression.

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is s...

ISIS Technical Report Support Vector Machines for Classification and Regression

Support Vector Regression Machines

A new regression technique based on Vapnik’s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend...

Properties of Support Vector Machines for Regression Properties of Support Vector Machines for Regression

In this report we show that the-tube size in Support Vector Machine (SVM) for regression is 2= p 1 + jjwjj 2. By using this result we show that, in the case all the data points are inside the-tube, minimizing jjwjj 2 in SVM for regression is equivalent to maximizing the distance between the approximating hyperplane and the farest points in the training set. Moreover, in the most general setting...

Classiication Properties of Support Vector Machines for Regression Classiication Properties of Support Vector Machines for Regression

In this report we show some consequences of the work done by Pontil et al. in 1]. In particular we show that in the same hypotheses of the theorem proved in their paper, the optimal approximating hyperplane f R found by SVM regression classiies the data. This means that y i f R (x i) > 0 for points which live externally to the margin between the two classes or points which live internally to th...