141 search results - page 8 / 29 » Learning r-of-k Functions by Boosting |
EUROCOLT
1995
Springer
13 years 10 months ago
1995
Springer
k. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multi...
COLT
2008
Springer
13 years 8 months ago
2008
Springer
Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weak-learnability. The starting po...
ICML
2008
IEEE
14 years 7 months ago
2008
IEEE
A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set. This class...
ICML
2007
IEEE
14 years 7 months ago
2007
IEEE
We study the problem of learning a classification task in which only a dissimilarity function of the objects is accessible. That is, data are not represented by feature vectors bu...
ICML
2004
IEEE
14 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...