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» Regularization by Adding Redundant Features
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ICML
2010
IEEE
13 years 6 months ago
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
CDES
2006
158views Hardware» more  CDES 2006»
13 years 6 months ago
A Double Precision Floating Point Multiplier Suitably Designed for FPGAs and ASICs
In this paper, a double precision IEEE 754 floating-point multiplier with high speed and low power is presented. The bottleneck of any double precision floatingpoint multiplier des...
Himanshu Thapliyal, Vishal Verma, Hamid R. Arabnia
IWANN
2005
Springer
13 years 10 months ago
Heuristic Search over a Ranking for Feature Selection
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in hig...
Roberto Ruiz, José Cristóbal Riquelm...
COMCOM
2006
123views more  COMCOM 2006»
13 years 4 months ago
Modeling the performance of flooding in wireless multi-hop Ad hoc networks
One feature common to most existing routing protocols for wireless mobile ad hoc networks, or MANETs, is the need to flood control messages network-wide during the route acquisiti...
Kumar Viswanath, Katia Obraczka
ISNN
2007
Springer
13 years 11 months ago
Memetic Algorithms for Feature Selection on Microarray Data
In this paper, we present two novel memetic algorithms (MAs) for gene selection. Both are synergies of Genetic Algorithm (wrapper methods) and local search methods (filter methods...
Zexuan Zhu, Yew-Soon Ong