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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
16 years 1 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai
IWBRS
2005
Springer
168views Biometrics» more  IWBRS 2005»
16 years 1 months ago
Gabor Feature Selection for Face Recognition Using Improved AdaBoost Learning
Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual infor...
LinLin Shen, Li Bai, Daniel Bardsley, Yangsheng Wa...
RTAS
2006
IEEE
16 years 1 months ago
Switch Scheduling and Network Design for Real-Time Systems
The rapid need for high bandwidth and low latency communication in distributed real-time systems is driving system architects towards high-speed switches developed for high volume...
Sathish Gopalakrishnan, Marco Caccamo, Lui Sha
CF
2008
ACM
15 years 9 months ago
Cell-SWat: modeling and scheduling wavefront computations on the cell broadband engine
This paper contributes and evaluates a model and a methodology for implementing parallel wavefront algorithms on the Cell Broadband Engine. Wavefront algorithms are vital in sever...
Ashwin M. Aji, Wu-chun Feng, Filip Blagojevic, Dim...
AAAI
2008
15 years 10 months ago
Trace Ratio Criterion for Feature Selection
Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a fe...
Feiping Nie, Shiming Xiang, Yangqing Jia, Changshu...