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NIPS
1994
15 years 5 months ago
Active Learning with Statistical Models
For many types of machine learning algorithms, one can compute the statistically optimal" way to select training data. In this paper, we review how optimal data selection tec...
David A. Cohn, Zoubin Ghahramani, Michael I. Jorda...
IJCAI
1989
15 years 5 months ago
Using and Refining Simplifications: Explanation-Based Learning of Plans in Intractable Domains
This paper describes an explanation-based approach lo learning plans despite a computationally intractable domain theory. In this approach, the system learns an initial plan using...
Steve A. Chien
ICPR
2010
IEEE
15 years 4 months ago
Adaptive Incremental Learning with an Ensemble of Support Vector Machines
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incre...
Marcelo N. Kapp, Robert Sabourin, Patrick Maupin
JMLR
2006
123views more  JMLR 2006»
15 years 4 months ago
Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
In this paper, we propose a number of adaptive prototype learning (APL) algorithms. They employ the same algorithmic scheme to determine the number and location of prototypes, but...
Fu Chang, Chin-Chin Lin, Chi-Jen Lu
JSW
2007
112views more  JSW 2007»
15 years 4 months ago
The Challenge of Training New Architects: an Ontological and Reinforcement-Learning Methodology
— This paper describes the importance of new skilled architects in the discipline of Software and Enterprise Architecture. Architects are often idealized as super heroes with a l...
Anabel Fraga, Juan Lloréns