3718 search results - page 449 / 744 » On learning with dissimilarity functions |
176
Voted
ICML
2005
IEEE
16 years 7 months ago
2005
IEEE
In this paper, we present a new interpretation of AdaBoost.ECC and AdaBoost.OC. We show that AdaBoost.ECC performs stage-wise functional gradient descent on a cost function, defin...
187
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ICML
2004
IEEE
16 years 7 months ago
2004
IEEE
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, no...
172
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ICML
2004
IEEE
16 years 7 months ago
2004
IEEE
Boosting is a popular approach for building accurate classifiers. Despite the initial popular belief, boosting algorithms do exhibit overfitting and are sensitive to label noise. ...
155
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ICML
2004
IEEE
16 years 7 months ago
2004
IEEE
Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
197
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ICML
2003
IEEE
16 years 7 months ago
2003
IEEE
Convex programming involves a convex set F Rn and a convex cost function c : F R. The goal of convex programming is to find a point in F which minimizes c. In online convex prog...