We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an-class problem, the DDAG contains � classifiers, one for each pair of classes. We present a VC analysis of the case when

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

Supervised classification algorithms aim at producing a learning model from a labeled training set. Various successful techniques have been proposed to solve the problem in the binary classification case. The multiclass classification case is more delicate, as many of the algorithms were introduced

Abstract-Supervised learning is based on the target value or the desired outputs. Various successful techniques have been proposed to solve the problem in the binary classification case. The multiclass classification case is more delicate one. In this short survey we investigate the various

out that one can lose consistency in generalizing a binary classification method to deal with multiple classes. We study a rich family of multiclass methods and provide a necessary and sufficient condition for their consistency. We illustrate our approach by applying it to some multiclass methods

A novel approach to multiclass tumor classification using Artificial Neural Networks (ANNs) was introduced in a recent paper [1]. The method successfully classified and diagnosed small, round blue cell tumors (SRBCTs) of childhood into four distinct categories, neuroblastoma (NB), rhabdomyosarcoma

, perhaps other simple linear classificationmethodsmaygive similar results, and with appropriate feature selection, fewer and more biologically salient genes can be retrieved. In an analysis similar to that of Yeang et al [4], multiclass classification is performed using 3 binary classifiers (k

I present a multiclass classification method for 3D shapes in this project. First, D2 shape distribution is used to generate discriminative representation of 3D shapes, which is developed to be invariant to rigid transformations and scalings. Next I employ the all-versus-all approach to decompose

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study

Many machine learning applications employ a multiclass clas-sification stage that uses multiple binary linear classifiers as build-ing blocks. Among these, commonly used strategies such as one-vs-one classification can require learning a large number of hyper-planes, even when the number of classes