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NIPS
2004
9 years 3 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 ...
JMLR
2006
99views more  JMLR 2006»
9 years 2 months ago
Worst-Case Analysis of Selective Sampling for Linear Classification
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
EOR
2007
99views more  EOR 2007»
9 years 2 months ago
Learning lexicographic orders
The purpose of this paper is to learn the order of criteria of lexicographic decision under various reasonable assumptions. We give a sample evaluation and an oracle based algorit...
József Dombi, Csanád Imreh, Ná...
GECCO
2010
Springer
155views Optimization» more  GECCO 2010»
9 years 7 months ago
Negative selection algorithms without generating detectors
Negative selection algorithms are immune-inspired classifiers that are trained on negative examples only. Classification is performed by generating detectors that match none of ...
Maciej Liskiewicz, Johannes Textor
RTAS
2008
IEEE
9 years 8 months ago
Using Trace Scratchpads to Reduce Execution Times in Predictable Real-Time Architectures
Instruction scratchpads have been previously suggested as a way to reduce the worst case execution time (WCET) of hard real-time programs without introducing the analysis issues p...
Jack Whitham, Neil C. Audsley
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