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ICANN
2009
Springer
14 years 1 months ago
Learning SVMs from Sloppily Labeled Data
This paper proposes a modelling of Support Vector Machine (SVM) learning to address the problem of learning with sloppy labels. In binary classification, learning with sloppy labe...
Guillaume Stempfel, Liva Ralaivola
ICML
2010
IEEE
13 years 10 months ago
Deep networks for robust visual recognition
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...
Yichuan Tang, Chris Eliasmith
MI
1993
13 years 10 months ago
Controlling a steel mill with BOXES
We describe an application of the BOXES learning algorithm of Michie and Chambers (1968) to a large-scale, real-world problem, namely, learning to control a steel mill. By applyin...
Michael McGarity, Claude Sammut, David P. Clements
ACL
2006
13 years 10 months ago
Using String-Kernels for Learning Semantic Parsers
We present a new approach for mapping natural language sentences to their formal meaning representations using stringkernel-based classifiers. Our system learns these classifiers ...
Rohit J. Kate, Raymond J. Mooney
ICIP
2010
IEEE
13 years 6 months ago
Learning denoising bounds for noisy images
In [1], we derived an expression for the fundamental limit to image denoising assuming that the noise-free image is available. In this paper, we propose an estimator for the bound...
Priyam Chatterjee, Peyman Milanfar