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CORR
2011
Springer
183views Education» more  CORR 2011»
7 years 11 months ago
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the tra...
Foster J. Provost, Gary M. Weiss
ML
2000
ACM
8 years 7 months ago
Maximizing Theory Accuracy Through Selective Reinterpretation
Existing methods for exploiting awed domain theories depend on the use of a su ciently large set of training examples for diagnosing and repairing aws in the theory. In this paper,...
Shlomo Argamon-Engelson, Moshe Koppel, Hillel Walt...
JASIS
2000
143views more  JASIS 2000»
8 years 7 months ago
Discovering knowledge from noisy databases using genetic programming
s In data mining, we emphasize the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). T...
Man Leung Wong, Kwong-Sak Leung, Jack C. Y. Cheng
CORR
2002
Springer
99views Education» more  CORR 2002»
8 years 7 months ago
The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning
A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature ...
Peter D. Turney
SIGIR
2008
ACM
8 years 7 months ago
Personalized active learning for collaborative filtering
Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most infor...
Abhay Harpale, Yiming Yang
SIGIR
2008
ACM
8 years 7 months ago
Bilingual topic aspect classification with a few training examples
This paper explores topic aspect (i.e., subtopic or facet) classification for English and Chinese collections. The evaluation model assumes a bilingual user who has found document...
Yejun Wu, Douglas W. Oard
JMLR
2006
132views more  JMLR 2006»
8 years 7 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof
JMLR
2008
89views more  JMLR 2008»
8 years 7 months ago
An Error Bound Based on a Worst Likely Assignment
This paper introduces a new PAC transductive error bound for classification. The method uses information from the training examples and inputs of working examples to develop a set...
Eric Bax, Augusto Callejas
AAAI
1997
8 years 8 months ago
Active Learning with Committees for Text Categorization
In many real-world domains, supervised learning requires a large number of training examples. In this paper, we describe an active learning method that uses a committee of learner...
Ray Liere, Prasad Tadepalli
FLAIRS
1998
8 years 8 months ago
Interactively Training Pixel Classifiers
Manual generation of training examples for supervised learning is an expensive process. One way to reduce this cost is to produce training instances that are highly informative. T...
Justus H. Piater, Edward M. Riseman, Paul E. Utgof...
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