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» Improved Learning of AC0 Functions
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IEEEPACT
2008
IEEE
14 years 2 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...
PAMI
2008
161views more  PAMI 2008»
13 years 7 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...
BMCBI
2007
95views more  BMCBI 2007»
13 years 7 months ago
Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices
Background: Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-ev...
Roger A. Craig, Li Liao
ICONIP
2008
13 years 9 months ago
On Node-Fault-Injection Training of an RBF Network
Abstract. While injecting fault during training has long been demonstrated as an effective method to improve fault tolerance of a neural network, not much theoretical work has been...
John Sum, Chi-Sing Leung, Kevin Ho
ML
2006
ACM
132views Machine Learning» more  ML 2006»
13 years 7 months ago
A suffix tree approach to anti-spam email filtering
We present an approach to email filtering based on the suffix tree data structure. A method for the scoring of emails using the suffix tree is developed and a number of scoring and...
Rajesh Pampapathi, Boris Mirkin, Mark Levene