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» New Algorithms for Learning in Presence of Errors
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ALT
1999
Springer
13 years 11 months ago
PAC Learning with Nasty Noise
We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz
ICES
1998
Springer
131views Hardware» more  ICES 1998»
13 years 11 months ago
Aspects of Digital Evolution: Geometry and Learning
In this paper we present a new chromosome representation for evolving digital circuits. The representation is based very closely on the chip architecture of the Xilinx 6216 FPGA. W...
Julian F. Miller, Peter Thomson
TSP
2010
13 years 2 months ago
Analysis of the Stereophonic LMS/Newton Algorithm and Impact of Signal Nonlinearity on Its Convergence Behavior
The strong cross-correlation that exists between the two input audio channels makes the problem of stereophonic acoustic echo cancellation (AEC) complex and challenging to solve. R...
Harsha I. K. Rao, Behrouz Farhang-Boroujeny
ICCV
2009
IEEE
13 years 5 months ago
A robust boosting tracker with minimum error bound in a co-training framework
The varying object appearance and unlabeled data from new frames are always the challenging problem in object tracking. Recently machine learning methods are widely applied to tra...
Rong Liu, Jian Cheng, Hanqing Lu
STOC
1993
ACM
117views Algorithms» more  STOC 1993»
13 years 11 months ago
Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Michael J. Kearns