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IJDMMM
2008
87views more  IJDMMM 2008»
15 years 3 months ago
Is an ordinal class structure useful in classifier learning?
In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, t...
Jens C. Huhn, Eyke Hüllermeier
KDD
2009
ACM
173views Data Mining» more  KDD 2009»
16 years 3 months ago
The offset tree for learning with partial labels
We present an algorithm, called the offset tree, for learning in situations where a loss associated with different decisions is not known, but was randomly probed. The algorithm i...
Alina Beygelzimer, John Langford
ICML
2009
IEEE
16 years 3 months ago
ABC-boost: adaptive base class boost for multi-class classification
We propose abc-boost (adaptive base class boost) for multi-class classification and present abc-mart, an implementation of abcboost, based on the multinomial logit model. The key ...
Ping Li
CORR
2010
Springer
94views Education» more  CORR 2010»
15 years 3 months ago
Tight Sample Complexity of Large-Margin Learning
We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the -adapted-dimension, which...
Sivan Sabato, Nathan Srebro, Naftali Tishby
JMLR
2008
117views more  JMLR 2008»
15 years 3 months ago
Active Learning by Spherical Subdivision
We introduce a computationally feasible, "constructive" active learning method for binary classification. The learning algorithm is initially formulated for separable cl...
Falk-Florian Henrich, Klaus Obermayer