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ICML
2005
IEEE
15 years 10 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III
IFIP12
2008
14 years 11 months ago
Bayesian Networks Optimization Based on Induction Learning Techniques
Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learni...
Paola Britos, Pablo Felgaer, Ramón Garc&iac...
ICML
2009
IEEE
15 years 10 months ago
Learning kernels from indefinite similarities
Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similariti...
Yihua Chen, Maya R. Gupta, Benjamin Recht
TMA
2010
Springer
137views Management» more  TMA 2010»
14 years 7 months ago
K-Dimensional Trees for Continuous Traffic Classification
Abstract. The network measurement community has proposed multiple machine learning (ML) methods for traffic classification during the last years. Although several research works ha...
Valentín Carela-Español, Pere Barlet...
NN
2006
Springer
146views Neural Networks» more  NN 2006»
14 years 9 months ago
Comparison of relevance learning vector quantization with other metric adaptive classification methods
The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability of metric adaptation...
Thomas Villmann, Frank-Michael Schleif, Barbara Ha...