Results 1  10
of
1,315
Unconfused Ultraconservative Multiclass Algorithms
"... We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The twoclass version of this problem was studied a few years ago by, e.g. Bylander (1994) and Blum et al. (1996): in these contributions, the proposed approaches to fight the noise revolve around a Per ..."
Abstract
 Add to MetaCart
Perceptron learning scheme fed with peculiar examples computed through a weighted average of points from the noisy training set. We propose to build upon these approaches and we introduce a new algorithm called UMA (for Unconfused Multiclass additive Algorithm) which may be seen as a generalization
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
Abstract

Cited by 561 (20 self)
 Add to MetaCart
We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class
On the algorithmic implementation of multiclass kernelbased vector machines
 Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernelbased 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 ob ..."
Abstract

Cited by 559 (13 self)
 Add to MetaCart
In this paper we describe the algorithmic implementation of multiclass kernelbased 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
Solving multiclass learning problems via errorcorrecting output codes
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 1995
"... Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass l ..."
Abstract

Cited by 726 (8 self)
 Add to MetaCart
learning problems include direct application of multiclass algorithms such as the decisiontree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed
A Scalable Multiclass Algorithm for Node Classification
"... We introduce a scalable algorithm, MUCCA for multiclass node classification in weighted graphs. Unlike previously proposed methods for the same task, MUCCA works in time linear in the number of nodes. Our approach is based on a gametheoretic formulation of the problem in which the test labels are e ..."
Abstract
 Add to MetaCart
We introduce a scalable algorithm, MUCCA for multiclass node classification in weighted graphs. Unlike previously proposed methods for the same task, MUCCA works in time linear in the number of nodes. Our approach is based on a gametheoretic formulation of the problem in which the test labels
A Scalable Multiclass Algorithm for Node Classification
, 2011
"... We introduce a scalable algorithm, MUCCA for multiclass node classification in weighted graphs. Unlike previously proposed methods for the same task, MUCCA works in time linear in the number of nodes. Our approach is based on a gametheoretic formulation of the problem in which the test labels are ..."
Abstract
 Add to MetaCart
We introduce a scalable algorithm, MUCCA for multiclass node classification in weighted graphs. Unlike previously proposed methods for the same task, MUCCA works in time linear in the number of nodes. Our approach is based on a gametheoretic formulation of the problem in which the test labels
Ultraconservative Online Algorithms for Multiclass Problems
 Journal of Machine Learning Research
, 2001
"... In this paper we study online classification algorithms for multiclass problems in the mistake bound model. The hypotheses we use maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarityscore between each prototype and the input instance and th ..."
Abstract

Cited by 320 (21 self)
 Add to MetaCart
In this paper we study online classification algorithms for multiclass problems in the mistake bound model. The hypotheses we use maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarityscore between each prototype and the input instance
Large margin dags for multiclass classification
 Advances in Neural Information Processing Systems 12
, 2000
"... We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many twoclass classifiers into a multiclass classifier. For anclass problem, the DDAG contains � classifiers, one for each pair of classes. We present a VC analysis of the case when the nod ..."
Abstract

Cited by 374 (1 self)
 Add to MetaCart
We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many twoclass classifiers into a multiclass classifier. For anclass problem, the DDAG contains � classifiers, one for each pair of classes. We present a VC analysis of the case when
Improved Boosting Algorithms Using Confidencerated Predictions
 MACHINE LEARNING
, 1999
"... We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find impr ..."
Abstract

Cited by 940 (26 self)
 Add to MetaCart
out to be identical to one proposed by Kearns and Mansour. We focus next on how to apply the new boosting algorithms to multiclass classification problems, particularly to the multilabel case in which each example may belong to more than one class. We give two boosting methods for this problem, plus
BoosTexter: A Boostingbased System for Text Categorization
"... This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text catego ..."
Abstract

Cited by 667 (20 self)
 Add to MetaCart
This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text
Results 1  10
of
1,315