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» Computational model for amygdala neural networks
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IJCNN
2008
IEEE
15 years 6 months ago
Sparse kernel density estimator using orthogonal regression based on D-Optimality experimental design
— A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experi...
Sheng Chen, Xia Hong, Chris J. Harris
IJCNN
2008
IEEE
15 years 6 months ago
A formula of equations of states in singular learning machines
Abstract— Almost all learning machines used in computational intelligence are not regular but singular statistical models, because they are nonidentifiable and their Fisher info...
Sumio Watanabe
IJCNN
2007
IEEE
15 years 6 months ago
Evaluation of Performance Measures for SVR Hyperparameter Selection
— To obtain accurate modeling results, it is of primal importance to find optimal values for the hyperparameters in the Support Vector Regression (SVR) model. In general, we sea...
Koen Smets, Brigitte Verdonk, Elsa Jordaan
CIBCB
2006
IEEE
15 years 5 months ago
Visualization of Support Vector Machines with Unsupervised Learning
– The visualization of support vector machines in realistic settings is a difficult problem due to the high dimensionality of the typical datasets involved. However, such visuali...
Lutz Hamel
CIG
2005
IEEE
15 years 5 months ago
Nannon: A Nano Backgammon for Machine Learning Research
A newly designed game is introduced, which feels like Backgammon, but has a simplified rule set. Unlike earlier attempts at simplifying the game, Nannon maintains enough features a...
Jordan B. Pollack