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» Evaluating algorithms that learn from data streams
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ICML
2004
IEEE
16 years 4 months ago
Learning and evaluating classifiers under sample selection bias
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model...
Bianca Zadrozny
EDBT
2008
ACM
138views Database» more  EDBT 2008»
15 years 5 months ago
Potential-driven load distribution for distributed data stream processing
A large class of applications require real-time processing of continuous stream data resulting in the development of data stream management systems (DSMS). Since many of these app...
Weihan Wang, Mohamed A. Sharaf, Shimin Guo, M. Tam...
ATAL
2004
Springer
15 years 9 months ago
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
We present GAMUT1 , a suite of game generators designed for testing game-theoretic algorithms. We explain why such a generator is necessary, offer a way of visualizing relationshi...
Eugene Nudelman, Jennifer Wortman, Yoav Shoham, Ke...
SP
2008
IEEE
159views Security Privacy» more  SP 2008»
15 years 3 months ago
Inferring neuronal network connectivity from spike data: A temporal data mining approach
Abstract. Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology an...
Debprakash Patnaik, P. S. Sastry, K. P. Unnikrishn...
APIN
2004
116views more  APIN 2004»
15 years 3 months ago
Neural Learning from Unbalanced Data
This paper describes the result of our study on neural learning to solve the classification problems in which data is unbalanced and noisy. We conducted the study on three differen...
Yi Lu Murphey, Hong Guo, Lee A. Feldkamp