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» Experimental Design for Variable Selection in Data Bases
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BMCBI
2008
166views more  BMCBI 2008»
15 years 21 days ago
Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual informa
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
Weijun Luo, Kurt D. Hankenson, Peter J. Woolf
98
Voted
ICASSP
2011
IEEE
14 years 4 months ago
SVM feature selection for multidimensional EEG data
In many machine learning applications, like Brain - Computer Interfaces (BCI), only high-dimensional noisy data are available rendering the discrimination task non-trivial. In thi...
Nisrine Jrad, Ronald Phlypo, Marco Congedo
107
Voted
GLOBECOM
2008
IEEE
15 years 7 months ago
Cross-Layer Design of Optimal Adaptation Technique over Selection-Combining Diversity Nakagami-m Fading Channels
— Adaptive modulation and antenna diversity are two important enabling techniques for future wireless network to meet demand for high data rate transmission. We study a Markov de...
Ashok K. Karmokar, Vijay K. Bhargava
DAC
1996
ACM
15 years 4 months ago
A Boolean Approach to Performance-Directed Technology Mapping for LUT-Based FPGA Designs
Abstract -- This paper presents a novel, Boolean approach to LUTbased FPGA technology mapping targeting high performance. As the core of the approach, we have developed a powerful ...
Christian Legl, Bernd Wurth, Klaus Eckl
ICCAD
2007
IEEE
96views Hardware» more  ICCAD 2007»
15 years 9 months ago
Monte-Carlo driven stochastic optimization framework for handling fabrication variability
Increasing effects of fabrication variability have inspired a growing interest in statistical techniques for design optimization. In this work, we propose a Monte-Carlo driven sto...
Vishal Khandelwal, Ankur Srivastava