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» Evaluating learning algorithms and classifiers
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ACL
2010
15 years 2 months ago
Inducing Domain-Specific Semantic Class Taggers from (Almost) Nothing
This research explores the idea of inducing domain-specific semantic class taggers using only a domain-specific text collection and seed words. The learning process begins by indu...
Ruihong Huang, Ellen Riloff
CVPR
2010
IEEE
16 years 23 days ago
Learning from Interpolated Images using Neural Networks for Digital Forensics
Interpolated images have data redundancy, and special correlation exists among neighboring pixels, which is a crucial clue in digital forensics. We design a neural network based f...
Yizhen Huang, Na Fan
SIGIR
2008
ACM
15 years 4 months ago
Classifiers without borders: incorporating fielded text from neighboring web pages
Accurate web page classification often depends crucially on information gained from neighboring pages in the local web graph. Prior work has exploited the class labels of nearby p...
Xiaoguang Qi, Brian D. Davison
COLT
2001
Springer
15 years 9 months ago
Learning Additive Models Online with Fast Evaluating Kernels
Abstract. We develop three new techniques to build on the recent advances in online learning with kernels. First, we show that an exponential speed-up in prediction time per trial ...
Mark Herbster
ATAL
2004
Springer
15 years 10 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...