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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12992 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
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 ..."
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Cited by 721 (8 self)
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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
Using Output Codes to Boost Multiclass Learning Problems
 MACHINE LEARNING: PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE, 1997 (ICML97)
, 1997
"... This paper describes a new technique for solving multiclass learning problems by combining Freund and Schapire's boosting algorithm with the main ideas of Dietterich and Bakiri's method of errorcorrecting output codes (ECOC). Boosting is a general method of improving the accuracy of a giv ..."
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Cited by 112 (8 self)
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This paper describes a new technique for solving multiclass learning problems by combining Freund and Schapire's boosting algorithm with the main ideas of Dietterich and Bakiri's method of errorcorrecting output codes (ECOC). Boosting is a general method of improving the accuracy of a
From Margins to Probabilities in Multiclass Learning Problems
 Proceedings of the 15th annual conference on arti intelligence
, 2002
"... We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using marginbased binary classifiers. An important open problem in this context is how to measure the distance between class codewords and the outputs of the classifiers. In this paper we ..."
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Cited by 5 (3 self)
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We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using marginbased binary classifiers. An important open problem in this context is how to measure the distance between class codewords and the outputs of the classifiers. In this paper we
On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems
"... 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 onevsall and allpairs methods, and the errorcorrecting output code methods of Dietterich and Bakiri (1995), can all be viewed a ..."
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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 onevsall and allpairs methods, and the errorcorrecting output code methods of Dietterich and Bakiri (1995), can all be viewed
Musical QuerybyDescription as a Multiclass Learning Problem
 In Proc. IEEE Multimedia Signal Processing Conference (MMSP
, 2002
"... We present the querybydescription (QBD) component of "Kandem," a timeaware music retrieval system. The QBD system we describe learns a relation between descriptive text concerning a musical artist and their actual acoustic output, making such queries as "Play me something loud with ..."
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Cited by 37 (2 self)
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We present the querybydescription (QBD) component of "Kandem," a timeaware music retrieval system. The QBD system we describe learns a relation between descriptive text concerning a musical artist and their actual acoustic output, making such queries as "Play me something loud
Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
, 2000
"... In the framework of decomposition methods for multiclass classifiation problems, error correcting output codes (ECOC) can be fruitfully used as codewords for coding classes in order to enhance the generalization capability of learning machines. The effectiveness of error correcting output codes depe ..."
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Cited by 20 (9 self)
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In the framework of decomposition methods for multiclass classifiation problems, error correcting output codes (ECOC) can be fruitfully used as codewords for coding classes in order to enhance the generalization capability of learning machines. The effectiveness of error correcting output codes
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 ..."
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Cited by 555 (20 self)
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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
A Comparison of Methods for Multiclass Support Vector Machines
 IEEE TRANS. NEURAL NETWORKS
, 2002
"... Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary class ..."
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Cited by 940 (22 self)
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classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using largescale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much
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 ..."
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Cited by 546 (13 self)
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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
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