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COLT
2006
Springer
13 years 11 months ago
Discriminative Learning Can Succeed Where Generative Learning Fails
Generative algorithms for learning classifiers use training data to separately estimate a probability model for each class. New items are classified by comparing their probabiliti...
Philip M. Long, Rocco A. Servedio
ICPR
2002
IEEE
14 years 9 months ago
The Combining Classifier: To Train or Not to Train?
When more than a single classifier has been trained for the same recognition problem the question arises how this set of classifiers may be combined into a final decision rule. Se...
Robert P. W. Duin
NAACL
2003
13 years 9 months ago
A Context-Sensitive Homograph Disambiguation in Thai Text-to-Speech Synthesis
Homograph ambiguity is an original issue in Text-to-Speech (TTS). To disambiguate homograph, several efficient approaches have been proposed such as part-of-speech (POS) n-gram, B...
Virongrong Tesprasit, Paisarn Charoenpornsawat, Vi...
CORR
2002
Springer
132views Education» more  CORR 2002»
13 years 7 months ago
Robust Feature Selection by Mutual Information Distributions
Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address question...
Marco Zaffalon, Marcus Hutter
JMLR
2010
104views more  JMLR 2010»
13 years 2 months ago
How to Explain Individual Classification Decisions
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the...
David Baehrens, Timon Schroeter, Stefan Harmeling,...