8 search results - page 1 / 2 » Regret Bounds for Hierarchical Classification with Linear-Th... |
COLT
2004
Springer
13 years 8 months ago
2004
Springer
We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce an incremental algorithm u...
NIPS
2004
13 years 6 months ago
2004
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
COLT
2008
Springer
13 years 6 months ago
2008
Springer
This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most t...
COLT
2010
Springer
13 years 2 months ago
2010
Springer
In standard online learning, the goal of the learner is to maintain an average loss that is "not too big" compared to the loss of the best-performing function in a fixed...
NIPS
2004
13 years 6 months ago
2004
We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchi...