Support Vector Machines for Multi-Attribute ABC Analysis

Multi-Layer Support Vector Machines

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Multi-prototype Support Vector Machines

We extend multiclass SVM to multiple prototypes per class. This allows to combine several simple models (e.g. linear models) in a principled way to get much more expressive decision functions. For this framework, we give a compact constrained quadratic formulation and we suggest an ef£cient algorithm for its optimization that guarantees a local minimum of the objective function. An annealed pro...

Multi-class Support Vector Machines

Cost-sensitive Attribute Value Acquisition for Support Vector Machines∗

We consider cost-sensitive attribute value acquisition in classification problems, where missing attribute values in test instances can be acquired at some cost. We examine this problem in the context of the support vector machine, employing a generic, iterative framework that aims to minimize both acquisition and misclassification costs. Under this framework, we propose an attribute value acqu...

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...