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» Evaluating algorithms that learn from data streams
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CVPR
2010
IEEE
15 years 2 months ago
P-N learning: Bootstrapping binary classifiers by structural constraints
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the ...
Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
124
Voted
ALT
1998
Springer
15 years 8 months ago
PAC Learning from Positive Statistical Queries
Learning from positive examples occurs very frequently in natural learning. The PAC learning model of Valiant takes many features of natural learning into account, but in most case...
François Denis
ERSHOV
2006
Springer
15 years 7 months ago
Streaming Networks for Coordinating Data-Parallel Programs
A new coordination language for distributed data-parallel programs is presented, call SNet. The intention of SNet is to introduce advanced structuring techniques into a coordinatio...
Clemens Grelck, Sven-Bodo Scholz, Alexander V. Sha...
ICMCS
2007
IEEE
112views Multimedia» more  ICMCS 2007»
15 years 10 months ago
Detecting Unsafe Driving Patterns using Discriminative Learning
We propose a discriminative learning approach for fusing multichannel sequential data with application to detect unsafe driving patterns from multi-channel driving recording data....
Yue Zhou, Wei Xu, Huazhong Ning, Yihong Gong, Thom...
ICDM
2008
IEEE
120views Data Mining» more  ICDM 2008»
15 years 10 months ago
Predicting Future Decision Trees from Evolving Data
Recognizing and analyzing change is an important human virtue because it enables us to anticipate future scenarios and thus allows us to act pro-actively. One approach to understa...
Mirko Böttcher, Martin Spott, Rudolf Kruse