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» Evaluating algorithms that learn from data streams
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NIPS
2003
15 years 8 months ago
Unsupervised Context Sensitive Language Acquisition from a Large Corpus
We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. Th...
Zach Solan, David Horn, Eytan Ruppin, Shimon Edelm...
NIPS
2008
15 years 8 months ago
Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization
The cluster assumption is exploited by most semi-supervised learning (SSL) methods. However, if the unlabeled data is merely weakly related to the target classes, it becomes quest...
Liu Yang, Rong Jin, Rahul Sukthankar
ASPLOS
2011
ACM
14 years 10 months ago
Sponge: portable stream programming on graphics engines
Graphics processing units (GPUs) provide a low cost platform for accelerating high performance computations. The introduction of new programming languages, such as CUDA and OpenCL...
Amir Hormati, Mehrzad Samadi, Mark Woh, Trevor N. ...
ICCV
2009
IEEE
1019views Computer Vision» more  ICCV 2009»
16 years 11 months ago
Similarity Functions for Categorization: from Monolithic to Category Specific
Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjuncti...
Boris Babenko, Steve Branson, Serge Belongie
DSN
2009
IEEE
16 years 1 months ago
Comparing anomaly-detection algorithms for keystroke dynamics
Keystroke dynamics—the analysis of typing rhythms to discriminate among users—has been proposed for detecting impostors (i.e., both insiders and external attackers). Since man...
Kevin S. Killourhy, Roy A. Maxion