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NIPS
2008
15 years 5 months ago
Generative versus discriminative training of RBMs for classification of fMRI images
Neuroimaging datasets often have a very large number of voxels and a very small number of training cases, which means that overfitting of models for this data can become a very se...
Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel...
IJPRAI
2002
93views more  IJPRAI 2002»
15 years 3 months ago
Improving Stability of Decision Trees
Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. B...
Mark Last, Oded Maimon, Einat Minkov
EMNLP
2009
15 years 2 months ago
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The res...
Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie...
PAMI
2008
170views more  PAMI 2008»
15 years 4 months ago
Unsupervised Category Modeling, Recognition, and Segmentation in Images
Suppose a set of arbitrary (unlabeled) images contains frequent occurrences of 2D objects from an unknown category. This paper is aimed at simultaneously solving the following rel...
Sinisa Todorovic, Narendra Ahuja
ISMIS
2005
Springer
15 years 9 months ago
Scalable Inductive Learning on Partitioned Data
With the rapid advancement of information technology, scalability has become a necessity for learning algorithms to deal with large, real-world data repositories. In this paper, sc...
Qijun Chen, Xindong Wu, Xingquan Zhu