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A Maximum Entropy Approach for Collaborative Filtering
A Hierarchical Densitybased Clustering Analysis of Auditory
Discriminative Mining of Gene Microarray Data
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achieve activation adaptive algorithm analysis applied approach approximation assumed average block channel classifier cluster combination compared complex component compression computed condition consider consists constraints convergence corresponding criterion data set deconvolution defined denotes distribution effect entropy equalizer equations error estimate example experiments Figure filter frequency function Gaussian given hidden IEEE improvement increases independent initial input introduced iterations layer learning linear machine matrix maximum mean measure method minimize mixture neural network node noise nonlinear Note observed obtained optimal output parameters patterns performance phase possible prediction presented probability problem proposed recognition REFERENCES region represents respectively samples scheme scores segmentation selection separation sequence shown shows signal simulation solution space speaker statistical step structure Table task techniques threshold transformation units update values variance vector verification weights