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CORR
2010
Springer
253views Education» more  CORR 2010»
13 years 7 months ago
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of t...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Yann LeCu...
APIN
2004
116views more  APIN 2004»
13 years 7 months ago
Neural Learning from Unbalanced Data
This paper describes the result of our study on neural learning to solve the classification problems in which data is unbalanced and noisy. We conducted the study on three differen...
Yi Lu Murphey, Hong Guo, Lee A. Feldkamp
FSS
2006
154views more  FSS 2006»
13 years 7 months ago
Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction
In this paper, a Sequential Adaptive Fuzzy Inference System called SAFIS is developed based on the functional equivalence between a radial basis function network and a fuzzy infer...
Hai-Jun Rong, N. Sundararajan, Guang-Bin Huang, P....
ICIP
2004
IEEE
14 years 9 months ago
Identification of insect damaged wheat kernels using transmittance images
We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as th...
A. Enis Çetin, Tom Pearson, Zehra Cataltepe
IWANN
1999
Springer
13 years 11 months ago
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
R. Matthew Kretchmar, Charles W. Anderson