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» Maximal Vector Computation in Large Data Sets
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SIGIR
2006
ACM
14 years 3 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
AGIS
2008
319views more  AGIS 2008»
13 years 9 months ago
An Efficient Algorithm for Raster-to-Vector Data Conversion
Data conversion from raster to vector (R2V) is a key function in Geographic Information Systems (GIS) and remote sensing (RS) image processing for integrating GIS and RS data. The...
Junhua Teng, Fahui Wang, Yu Liu
PRL
2006
114views more  PRL 2006»
13 years 9 months ago
Incremental training of support vector machines using hyperspheres
In the conventional incremental training of support vector machines, candidates for support vectors tend to be deleted if the separating hyperplane rotates as the training data ar...
Shinya Katagiri, Shigeo Abe
NIPS
2001
13 years 10 months ago
Active Learning in the Drug Discovery Process
We investigate the following data mining problem from Computational Chemistry: From a large data set of compounds, find those that bind to a target molecule in as few iterations o...
Manfred K. Warmuth, Gunnar Rätsch, Michael Ma...
ICPR
2008
IEEE
14 years 10 months ago
Efficient background modeling through incremental Support Vector Data Description
Background modeling is an essential and important part of many high-level video processing applications. Recently, the Support Vector Data Description (SVDD) has been introduced f...
Alireza Tavakkoli, Mircea Nicolescu, George Bebis,...