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» New Algorithms for Learning in Presence of Errors
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IJON
1998
158views more  IJON 1998»
13 years 8 months ago
Bayesian Kullback Ying-Yang dependence reduction theory
Bayesian Kullback Ying—Yang dependence reduction system and theory is presented. Via stochastic approximation, implementable algorithms and criteria are given for parameter lear...
Lei Xu
CIKM
2011
Springer
12 years 8 months ago
Toward interactive training and evaluation
Machine learning often relies on costly labeled data, and this impedes its application to new classification and information extraction problems. This has motivated the developme...
Gregory Druck, Andrew McCallum
EUSFLAT
2009
184views Fuzzy Logic» more  EUSFLAT 2009»
13 years 6 months ago
Recurrent Neural Kalman Filter Identification and Indirect Adaptive Control of a Continuous Stirred Tank Bioprocess
The aim of this paper is to propose a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Marquardt (L-M) algorithm of its learning capable to est...
Ieroham S. Baruch, Carlos Román Mariaca Gas...
HPCA
2009
IEEE
14 years 9 months ago
Eliminating microarchitectural dependency from Architectural Vulnerability
The Architectural Vulnerability Factor (AVF) of a hardware structure is the probability that a fault in the structure will affect the output of a program. AVF captures both microa...
Vilas Sridharan, David R. Kaeli
APCCAS
2006
IEEE
233views Hardware» more  APCCAS 2006»
14 years 2 months ago
Jointly Optimized Modulated-Transmitter and Receiver FIR MIMO Filters
— In recent years, several approaches have been proposed aiming the optimal joint design of finite impulse response (FIR) multiple-input multiple-output (MIMO) transmitter and r...
Guilherme Pinto, Paulo S. R. Diniz, Are Hjø...