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ICMLA
2009
15 years 2 months ago
Transformation Learning Via Kernel Alignment
This article proposes an algorithm to automatically learn useful transformations of data to improve accuracy in supervised classification tasks. These transformations take the for...
Andrew Howard, Tony Jebara
NN
2006
Springer
146views Neural Networks» more  NN 2006»
15 years 4 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...
ICGI
2004
Springer
15 years 9 months ago
Learning Stochastic Finite Automata
Abstract. Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of th...
Colin de la Higuera, José Oncina
FUZZIEEE
2007
IEEE
15 years 10 months ago
Mining and Predicting CpG islands
— A DNA sequence can be described as a string composed of four symbols: A, T, C and G. Each symbol represents a chemically distinct nucleotide molecule. Combinations of two nucle...
Christopher Previti, Oscar Harari, Coral del Val
BMCBI
2011
14 years 8 months ago
BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs
Background: The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed ...
Frank P. Y. Lin, Stephen Anthony, Thomas M. Polase...