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GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
13 years 11 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
IDA
2002
Springer
13 years 7 months ago
Boosting strategy for classification
This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifi...
Huma Lodhi, Grigoris J. Karakoulas, John Shawe-Tay...
JMLR
2006
120views more  JMLR 2006»
13 years 7 months ago
Kernel-Based Learning of Hierarchical Multilabel Classification Models
We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is...
Juho Rousu, Craig Saunders, Sándor Szedm&aa...
IAT
2009
IEEE
13 years 11 months ago
Confusion and Distance Metrics as Performance Criteria for Hierarchical Classification Spaces
When intelligent systems reason about complex problems with a large hierarchical classification space it is hard to evaluate system performance. For classification problems, differ...
Wilbert van Norden, Catholijn M. Jonker
NIPS
2008
13 years 9 months ago
Relative Margin Machines
In classification problems, Support Vector Machines maximize the margin of separation between two classes. While the paradigm has been successful, the solution obtained by SVMs is...
Pannagadatta K. Shivaswamy, Tony Jebara