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164
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ICML
1998
IEEE
16 years 4 months ago
Employing EM and Pool-Based Active Learning for Text Classification
This paper shows how a text classifier's need for labeled training documents can be reduced by taking advantage of a large pool of unlabeled documents. We modify the Query-by...
Andrew McCallum, Kamal Nigam
135
Voted
EMNLP
2004
15 years 4 months ago
Learning Hebrew Roots: Machine Learning with Linguistic Constraints
The morphology of Semitic languages is unique in the sense that the major word-formation mechanism is an inherently non-concatenative process of interdigitation, whereby two morph...
Ezra Daya, Dan Roth, Shuly Wintner
169
Voted
ML
2010
ACM
135views Machine Learning» more  ML 2010»
14 years 10 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
96
Voted
IAT
2007
IEEE
15 years 9 months ago
Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods
Phishing attack is a kind of identity theft which tries to steal confidential data like on-line bank account information. In a phishing attack scenario, attacker deceives users by...
Alireza Saberi, Mojtaba Vahidi, Behrouz Minaei-Bid...
125
Voted
ECCV
2008
Springer
16 years 5 months ago
Multiple Component Learning for Object Detection
Abstract. Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, a...
Boris Babenko, Pietro Perona, Piotr Dollár,...