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» Evaluating learning algorithms and classifiers
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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 5 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
ICASSP
2011
IEEE
14 years 8 months ago
Online feature selection and classification
This paper presents an online feature selection and classification algorithm. The algorithm is implemented for impact acoustics signals to sort hazelnut kernels. The classifier, w...
Habil Kalkan, Bayram Cetisli
TRECVID
2008
15 years 5 months ago
National Institute of Informatics, Japan at TRECVID 2008
This paper reports our experiments for TRECVID 2008 tasks: high level feature extraction, search and contentbased copy detection. For the high level feature extraction task, we use...
Duy-Dinh Le, Xiaomeng Wu, Shin'ichi Satoh, Sheetal...
MLCW
2005
Springer
15 years 10 months ago
Evaluating Predictive Uncertainty Challenge
This Chapter presents the PASCAL1 Evaluating Predictive Uncertainty Challenge, introduces the contributed Chapters by the participants who obtained outstanding results, and provide...
Joaquin Quiñonero Candela, Carl Edward Rasm...
ALT
2008
Springer
16 years 1 months ago
A Uniform Lower Error Bound for Half-Space Learning
Abstract. We give a lower bound for the error of any unitarily invariant algorithm learning half-spaces against the uniform or related distributions on the unit sphere. The bound i...
Andreas Maurer, Massimiliano Pontil