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ICML
2007
IEEE
16 years 7 months ago
Experimental perspectives on learning from imbalanced data
We present a comprehensive suite of experimentation on the subject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the pers...
Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napol...
ICML
2008
IEEE
16 years 7 months ago
Knows what it knows: a framework for self-aware learning
We introduce a learning framework that combines elements of the well-known PAC and mistake-bound models. The KWIK (knows what it knows) framework was designed particularly for its...
Lihong Li, Michael L. Littman, Thomas J. Walsh
ICML
2005
IEEE
16 years 7 months ago
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
Mark Girolami, Simon Rogers
ALT
2006
Springer
16 years 3 months ago
Unsupervised Slow Subspace-Learning from Stationary Processes
Abstract. We propose a method of unsupervised learning from stationary, vector-valued processes. A low-dimensional subspace is selected on the basis of a criterion which rewards da...
Andreas Maurer
189
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ICML
2009
IEEE
16 years 1 months ago
Rule learning with monotonicity constraints
In classification with monotonicity constraints, it is assumed that the class label should increase with increasing values on the attributes. In this paper we aim at formalizing ...
Wojciech Kotlowski, Roman Slowinski