Boosting Support Vector Machines for RGB-D Based Terrain Classification

Boosting Support Vector Machines

This paper presents a classification algorithm based on Support Vector Machines classifiers combined with Boosting techniques. This classifier presents a better performance in training time, a similar generalization and a similar model complexity but the model representation is more compact.

A QUADRATIC MARGIN-BASED MODEL FOR WEIGHTING FUZZY CLASSIFICATION RULES INSPIRED BY SUPPORT VECTOR MACHINES

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...

Support Vector Machines versus Boosting

Support Vector Machines (SVMs) and Adaptive Boosting (AdaBoost) are two successful classification methods. They are essentially the same as they both try to maximize the minimal margin on a training set. In this work, we present an even platform to compare these two learning algorithms in terms of their test error, margin distribution and generalization power. Two basic models of polynomials an...

Support Vector Machines et Classification d’ Images

Density Based Support Vector Machines for Classification

Support Vector Machines (SVM) is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification) of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classi...