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» Learning large margin classifiers locally and globally
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ICML
2007
IEEE
14 years 8 months ago
On learning with dissimilarity functions
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...
Liwei Wang, Cheng Yang, Jufu Feng
CIARP
2008
Springer
13 years 9 months ago
Learning and Forgetting with Local Information of New Objects
The performance of supervised learners depends on the presence of a relatively large labeled sample. This paper proposes an automatic ongoing learning system, which is able to inco...
Fernando Vázquez, José Salvador S&aa...
ICML
2008
IEEE
14 years 8 months ago
Fast solvers and efficient implementations for distance metric learning
In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed framework for distance metric lear...
Kilian Q. Weinberger, Lawrence K. Saul
ICCV
2009
IEEE
15 years 18 days ago
Weakly supervised discriminative localization and classification: a joint learning process
Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first ...
Minh Hoai Nguyen, Lorenzo Torresani, Fernando de l...
AAAI
1994
13 years 9 months ago
Hierarchical Chunking in Classifier Systems
Two standard schemes for learning in classifier systems have been proposed in the literature: the bucket brigade algorithm (BBA) and the profit sharing plan (PSP). The BBA is a lo...
Gerhard Weiß