Publication

Support Vector Machine for Multiclass Classification

1998
Reports

Abstract

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 with this problem and their efficiencies are measured empirically. This simple way of using SVMs to learn a $K$ -class classification problem consists in choosing the maximum applied to the outputs of $K$ SVMs solving a \textit{one-per-class} decomposition of the general problem. In the second part of this paper, more sophisticated techniques are suggested. On the one hand, a stacking of the $K$ SVMs with other classification techniques is proposed. On the other end, the \textit{one-per-class} decomposition scheme is replaced by more elaborated schemes based on error-correcting codes. An incremental algorithm for the elaboration of pertinent decomposition schemes is mentioned, which exploits the properties of SVMs for an efficient computation.

Official source

https://infoscience.epfl.ch/entities/publication/3b9e50ea-b4be-462e-bf0c-e805d336edc2

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1998
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3b9e50ea-b4be-462e-bf0c-e805d336edc2

About this result

Related concepts (32)

Multiclass classification

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Statistical classification

In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.