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» Maximal Vector Computation in Large Data Sets

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ICPR
2000
IEEE

251views computer vision» more ICPR 2000»

Data Condensation in Large Databases by Incremental Learning with Support Vector Machines

14 years 8 months ago

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An algorithmfor data condensation using support vector machines (SVM's)is presented. The algorithm extracts datapoints lying close to the class boundaries,whichform a much re...

Pabitra Mitra, C. A. Murthy, Sankar K. Pal

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ICNC
2005
Springer

149views Applied Computing» more ICNC 2005»

Training Data Selection for Support Vector Machines

14 years 1 months ago

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Abstract. In recent years, support vector machines (SVMs) have become a popular tool for pattern recognition and machine learning. Training a SVM involves solving a constrained qua...

Jigang Wang, Predrag Neskovic, Leon N. Cooper

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NIPS
2001

134views Information Technology» more NIPS 2001»

Spectral Relaxation for K-means Clustering

13 years 9 months ago

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The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to nd local minima. In this paper we sh...

Hongyuan Zha, Xiaofeng He, Chris H. Q. Ding, Ming ...

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BICOB
2009
Springer

110views Computational Biology» more BICOB 2009»

Detecting Motifs in a Large Data Set: Applying Probabilistic Insights to Motif Finding

14 years 2 months ago