ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We present a theoretical analysis of supervised ranking, providing necessary and sufficient conditions for the asymptotic consistency of algorithms based on minimizing a surrogate...
ICONIP
2004
13 years 6 months ago
2004
Boosting algorithm is understood as the gradient descent algorithm of a loss function. It is often pointed out that the typical boosting algorithm, Adaboost, is seriously affected ...
ACL
2006
13 years 6 months ago
2006
Lasso is a regularization method for parameter estimation in linear models. It optimizes the model parameters with respect to a loss function subject to model complexities. This p...
NIPS
2007
13 years 6 months ago
2007
We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems. Our approach...
SDM
2008
SIAM
13 years 6 months ago
2008
SIAM
This paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex los...
SDM
2010
SIAM
13 years 6 months ago
2010
SIAM
The Passive Aggressive framework [1] is a principled approach to online linear classification that advocates minimal weight updates i.e., the least required so that the current tr...
ECCV
2010
Springer
13 years 8 months ago
2010
Springer
Abstract. Visual dictionary learning and base (binary) classifier training are two basic problems for the recently most popular image categorization framework, which is based on t...
EUROCOLT
1999
Springer
13 years 8 months ago
1999
Springer
We consider algorithms for combining advice from a set of experts. In each trial, the algorithm receives the predictions of the experts and produces its own prediction. A loss func...
COLT
1999
Springer
13 years 8 months ago
1999
Springer
We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...
CIKM
2009
Springer
13 years 9 months ago
2009
Springer
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...