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» Evaluating learning algorithms and classifiers
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87
Voted
ICDAR
2003
IEEE
15 years 5 months ago
Mirror Image Learning for Autoassociative Neural Networks
This paper studies on the mirror image learning algorithm for the autoassociative neural networks and evaluates the performance by handwritten numeral recognition test. Each of th...
Shusaku Shimizu, Wataru Ohyama, Tetsushi Wakabayas...
115
Voted
ICDM
2003
IEEE
134views Data Mining» more  ICDM 2003»
15 years 5 months ago
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conve...
Bianca Zadrozny, John Langford, Naoki Abe
IJCAI
1993
15 years 1 months ago
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This pa...
Usama M. Fayyad, Keki B. Irani
74
Voted
ICASSP
2010
IEEE
14 years 10 months ago
Efficient online learning with individual learning-rates for phoneme sequence recognition
We describe a fast and efficient online algorithm for phoneme sequence speech recognition. Our method is using a discriminative training to update the model parameters one utteran...
Koby Crammer
89
Voted
ATAL
2006
Springer
15 years 4 months ago
Learning against multiple opponents
We address the problem of learning in repeated N-player (as opposed to 2-player) general-sum games. We describe an extension to existing criteria focusing explicitly on such setti...
Thuc Vu, Rob Powers, Yoav Shoham