B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifers. In Proc. of the 5th ACM Workshop on Computational Learning Theory, pages 144--152. ACM Press, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....In this paper we investigate the problem of inductive learning from the point of view of predicting variables of ordinal scale [3, 7, 5] a setting referred to as ranking learning or ordinal regression. We consider the problem of applying the large margin principle used in Support Vector methods [12, 1] to the ordinal regression problem while maintaining an (optimal) problem size linear in the number of training examples. Let x i be the set of training examples where j = 1# : # k denotes the class number, and i =1#: #i j is the index within each class. Let l = j i j be the total number of ....

....by the VC dimension h of the set of loss functions. The structural risk minimization (SRM) principle [12] minimizes a bound on the risk over a structure on the set of functions. The geometric interpretation for 2 class learning is to maximize the margin between the boundaries of the two sets [12, 1]. In our setting of ranking learning, there are k ; 1 margins to consider, thus there are two possible approaches to take on the large margin principle for ranking learning: fixed margin strategy: the margin to be maximized is the one defined by the closest (neighboring) pair of classes. ....

B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifers. In Proc. of the 5th ACM Workshop on Computational Learning Theory, pages 144--152. ACM Press, 1992.

....In this paper we investigate the problem of inductive learning from the point of view of predicting variables of ordinal scale [3, 7, 5] a setting referred to as ranking learning or ordinal regression. We consider the problem of applying the large margin principle used in Support Vector methods [11, 2] to the ordinal regression problem while maintaining an (optimal) problem size linear in the number of training examples. Ordinal regression may be viewed as a problem bridging between the two standard machine learning tasks of classification and (metric) regression. Let x i 2 R , i = 1; l, ....

....the actual risk R(ff) error measured on the test data) by keeping Remp (ff) fixed (in the ideal separable case it would be zero) while minimizing the confidence interval. The geometric interpretation for 2 class learning is to maximize the margin between the boundaries of the two sets [11, 2]. In our setting of ranking learning, there are k Gamma 1 margins to consider, thus there are two possible approaches to take on the large margin principle for ranking learning: ffl fixed margin strategy: the margin to be maximized is the one defined by the closest (neighboring) pair of ....

B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifers. In Proc. of the 5th ACM Workshop on Computational Learning Theory, pages 144--152. ACM Press, 1992.

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B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifers. In Proc. of the 5th ACM Workshop on Computational Learning Theory, pages 144--152. ACM Press, 1992.

No context found.

B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifers. In Proc. of the 5th ACM Workshop on Computational Learning Theory, pages 144--152. ACM Press, 1992.

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