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HIS
2008
15 years 4 months ago
REPMAC: A New Hybrid Approach to Highly Imbalanced Classification Problems
The class imbalance problem (when one of the classes has much less samples than the others) is of great importance in machine learning, because it corresponds to many critical app...
Hernán Ahumada, Guillermo L. Grinblat, Luca...
JMLR
2010
121views more  JMLR 2010»
14 years 9 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
ICML
2006
IEEE
16 years 3 months ago
Multiclass boosting with repartitioning
A multiclass classification problem can be reduced to a collection of binary problems with the aid of a coding matrix. The quality of the final solution, which is an ensemble of b...
Ling Li
EDM
2010
140views Data Mining» more  EDM 2010»
15 years 4 months ago
Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data
Current peer-review software lacks intelligence for responding to students' reviewing performance. As an example of an additional intelligent assessment component to such soft...
Wenting Xiong, Diane J. Litman, Christian D. Schun...
JMLR
2010
139views more  JMLR 2010»
14 years 9 months ago
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
Alternating Gibbs sampling is the most common scheme used for sampling from Restricted Boltzmann Machines (RBM), a crucial component in deep architectures such as Deep Belief Netw...
Guillaume Desjardins, Aaron C. Courville, Yoshua B...