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ACL
1994
15 years 8 months ago
A Markov Language Learning Model for Finite Parameter Spaces
This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explici...
Partha Niyogi, Robert C. Berwick
ECCC
2010
124views more  ECCC 2010»
15 years 6 months ago
Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas
Much work has been done on learning various classes of "simple" monotone functions under the uniform distribution. In this paper we give the first unconditional lower bo...
Vitaly Feldman, Homin K. Lee, Rocco A. Servedio
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
16 years 7 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
ALT
2004
Springer
16 years 3 months ago
Learning Languages from Positive Data and Negative Counterexamples
In this paper we introduce a paradigm for learning in the limit of potentially infinite languages from all positive data and negative counterexamples provided in response to the ...
Sanjay Jain, Efim B. Kinber
184
Voted
CRV
2009
IEEE
115views Robotics» more  CRV 2009»
16 years 1 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman