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» On the monotonization of the training set
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ICML
2003
IEEE
16 years 7 months ago
SimpleSVM
We present a fast iterative support vector training algorithm for a large variety of different formulations. It works by incrementally changing a candidate support vector set usin...
S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha...
WSOM
2009
Springer
16 years 1 months ago
Analytic Comparison of Self-Organising Maps
Abstract. SOMs have proven to be a very powerful tool for data analysis. However, comparing multiple SOMs trained on the same data set using different parameters or initialisation...
Rudolf Mayer, Robert Neumayer, Doris Baum, Andreas...
ICML
2005
IEEE
16 years 7 months ago
Large scale genomic sequence SVM classifiers
In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve suffici...
Bernhard Schölkopf, Gunnar Rätsch, S&oum...
ICML
2003
IEEE
16 years 7 months ago
Incorporating Diversity in Active Learning with Support Vector Machines
In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
Klaus Brinker
SDM
2009
SIAM
105views Data Mining» more  SDM 2009»
16 years 3 months ago
Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
Gerhard Paaß, Frank Reichartz