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» Evaluating learning algorithms and classifiers
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KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 4 months ago
Spectral domain-transfer learning
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, ...
ACL
2007
15 years 5 months ago
A Feature Based Approach to Leveraging Context for Classifying Newsgroup Style Discussion Segments
On a multi-dimensional text categorization task, we compare the effectiveness of a feature based approach with the use of a stateof-the-art sequential learning technique that has ...
Yi-Chia Wang, Mahesh Joshi, Carolyn Penstein Ros&e...
ICML
2010
IEEE
15 years 5 months ago
Boosting Classifiers with Tightened L0-Relaxation Penalties
We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the ...
Noam Goldberg, Jonathan Eckstein
158
Voted
ICML
2001
IEEE
16 years 4 months ago
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be mad...
Bianca Zadrozny, Charles Elkan
JMLR
2008
150views more  JMLR 2008»
15 years 3 months ago
Discriminative Learning of Max-Sum Classifiers
The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and obser...
Vojtech Franc, Bogdan Savchynskyy