Multiclass One-versus-One (OvO) SVM, which is constructed by assembling a group of binary classifiers, is usually treated as a black-box. The usual Multiclass Feature Selection (MFS) algorithm chooses an identical subset of features for every OvO SVM. We question whether the standard process of applying feature selection and then constructing the multiclass classifier is best. We propose that I...

Building robust and fast multiclass object detection systems is a important goal of computer vision. In the present paper we extend the well-known work of Viola and Jones on boosted cascade classifiers to the multiclass case with the goal of building multiclass and multiview object detectors. We propose to use nested cascades of multiclass boosted classifiers and we introduce the concept of cou...

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this p...

The present multiclass boosting algorithms are hard to deal with Chinese handwritten character recognition for the large amount of classes. Most of them are based on schemes of converting multiclass classification to multiple binary classifications and have high training complexity. The proposed multiclass boosting algorithm adopts the descriptive model based multiclass classifiers (Modified Qu...

Multiclass classification is an important and ongoing research subject in machine learning. Current support vector methods for multiclass classification implicitly assume that the parameters in the optimization problems are known exactly. However, in practice, the parameters have perturbations since they are estimated from the training data, which are usually subject to measurement noise. In th...

In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic objective function. Unlike most of previous approaches which typically decompose a multiclass probl...

A popular approach to solving multiclass learning problems is to reduce them to a set of binary classification problems through some output code matrix: the widely used one-vs-all and all-pairs methods, and the error-correcting output code methods of Dietterich and Bakiri (1995), can all be viewed as special cases of this approach. In this paper, we consider the question of statistical consiste...

The one-against-all reduction from multiclass classification to binary classification is a standard technique used to solve multiclass problems with binary classifiers. We show that modifying this technique in order to optimize its error transformation properties results in a superior technique, both experimentally and theoretically. This algorithm can also be used to solve a more general class...

The constraint classification framework captures many flavors of multiclass classification including winner-take-all multiclass classification, multilabel classification and ranking. We present a meta-algorithm for learning in this framework that learns via a single linear classifier in high dimension. We discuss distribution independent as well as margin-based generalization bounds and present...