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» Enhancing Density-Based Data Reduction Using Entropy
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95
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ICASSP
2009
IEEE
15 years 9 months ago
Using collective information in semi-supervised learning for speech recognition
Training accurate acoustic models typically requires a large amount of transcribed data, which can be expensive to obtain. In this paper, we describe a novel semi-supervised learn...
Balakrishnan Varadarajan, Dong Yu, Li Deng, Alex A...
MCS
2001
Springer
15 years 6 months ago
Automatic Model Selection in a Hybrid Perceptron/Radial Network
We provide several enhancements to our previously introduced algorithm for a sequential construction of a hybrid network of radial and perceptron hidden units [6]. At each stage, ...
Shimon Cohen, Nathan Intrator
99
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PR
2006
89views more  PR 2006»
15 years 2 months ago
Gaussian fields for semi-supervised regression and correspondence learning
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
Jakob J. Verbeek, Nikos A. Vlassis
128
Voted
CASES
2006
ACM
15 years 8 months ago
Adapting compilation techniques to enhance the packing of instructions into registers
The architectural design of embedded systems is becoming increasingly idiosyncratic to meet varying constraints regarding energy consumption, code size, and execution time. Tradit...
Stephen Hines, David B. Whalley, Gary S. Tyson
137
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ISCI
2008
124views more  ISCI 2008»
15 years 2 months ago
A weighted rough set based method developed for class imbalance learning
In this paper, we introduce weights into Pawlak rough set model to balance the class distribution of a data set and develop a weighted rough set based method to deal with the clas...
Jinfu Liu, Qinghua Hu, Daren Yu