Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines

Interval discriminant analysis using support vector machines

Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval–represented data. Most approaches for SVM learning of interval data use local kernels based on interval distances. We present here a novel approach, suitable for linear SVMs, which allows to deal with interval data without resorting to interval distances. The experimental results confirms the validity ...

On Model-Based Clustering, Classification, and Discriminant Analysis

The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attenti...

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

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...

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...