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» A linear memory algorithm for Baum-Welch training
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JMLR
2008
133views more  JMLR 2008»
13 years 5 months ago
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classi...
Suhrid Balakrishnan, David Madigan
KDD
2010
ACM
222views Data Mining» more  KDD 2010»
13 years 7 months ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...
AMDO
2004
Springer
13 years 9 months ago
On-the-Fly Training
Abstract. A new algorithm for the incremental learning and non-intrusive tracking of the appearance of a previously non-seen face is presented. The computation is done in a causal ...
Javier Melenchón, Lourdes Meler, Ignasi Iri...
PDCN
2004
13 years 6 months ago
K-Means VQ algorithm using a low-cost parallel cluster computing
It is well-known that the time and memory necessary to create a codebook from large training databases have hindered the vector quantization based systems for real applications. T...
Paulo Sergio Lopes de Souza, Alceu de Souza Britto...
ACIVS
2006
Springer
13 years 11 months ago
A Comparison of Nearest Neighbor Search Algorithms for Generic Object Recognition
The nearest neighbor (NN) classifier is well suited for generic object recognition. However, it requires storing the complete training data, and classification time is linear in ...
Ferid Bajramovic, Frank Mattern, Nicholas Butko, J...