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» Feature selection in a kernel space
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NIPS
2004
13 years 11 months ago
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
USS
2008
14 years 11 days ago
Selective Versioning in a Secure Disk System
Making vital disk data recoverable even in the event of OS compromises has become a necessity, in view of the increased prevalence of OS vulnerability exploits over the recent yea...
Swaminathan Sundararaman, Gopalan Sivathanu, Erez ...
DIS
2008
Springer
13 years 12 months ago
Unsupervised Classifier Selection Based on Two-Sample Test
We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a s...
Timo Aho, Tapio Elomaa, Jussi Kujala
ICPR
2010
IEEE
14 years 3 months ago
Adaptive Feature and Score Level Fusion Strategy Using Genetic Algorithms
Classifier fusion is considered as one of the best strategies for improving performances upon general purpose classification systems. On the other hand, fusion strategy space stro...
Wael Ben Soltana, Mohsen Ardabilian, Liming Chen, ...
PR
2007
104views more  PR 2007»
13 years 9 months ago
Optimizing resources in model selection for support vector machine
Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the...
Mathias M. Adankon, Mohamed Cheriet