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» Input Window Size and Neural Network Predictors
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128
Voted
NN
2002
Springer
125views Neural Networks» more  NN 2002»
15 years 3 months ago
Generalized relevance learning vector quantization
We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of ...
Barbara Hammer, Thomas Villmann
120
Voted
NIPS
2008
15 years 5 months ago
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning
Randomized neural networks are immortalized in this well-known AI Koan: In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. "What a...
Ali Rahimi, Benjamin Recht
151
Voted
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
15 years 8 months ago
Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of sma
Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient i...
Emily Rose Holzinger, Carrie C. Buchanan, Scott M....
137
Voted
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
15 years 7 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
153
Voted
PPOPP
2009
ACM
16 years 4 months ago
Mapping parallelism to multi-cores: a machine learning based approach
The efficient mapping of program parallelism to multi-core processors is highly dependent on the underlying architecture. This paper proposes a portable and automatic compiler-bas...
Zheng Wang, Michael F. P. O'Boyle