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» The Inefficiency of Batch Training for Large Training Sets
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SIGIR
2006
ACM
14 years 2 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
FGR
2008
IEEE
264views Biometrics» more  FGR 2008»
13 years 10 months ago
Large scale learning and recognition of faces in web videos
The phenomenal growth of video on the web and the increasing sparseness of meta information associated with it forces us to look for signals from the video content for search/info...
Ming Zhao 0003, Jay Yagnik, Hartwig Adam, David Ba...
KDD
2004
ACM
285views Data Mining» more  KDD 2004»
14 years 2 months ago
Effective localized regression for damage detection in large complex mechanical structures
In this paper, we propose a novel data mining technique for the efficient damage detection within the large-scale complex mechanical structures. Every mechanical structure is defi...
Aleksandar Lazarevic, Ramdev Kanapady, Chandrika K...
BMCBI
2010
110views more  BMCBI 2010»
13 years 9 months ago
MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes
Background: The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitop...
Andrew J. Bordner, Hans D. Mittelmann
NIPS
2004
13 years 10 months ago
Parallel Support Vector Machines: The Cascade SVM
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. I...
Hans Peter Graf, Eric Cosatto, Léon Bottou,...