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» Learning by Discovering Concept Hierarchies
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PAMI
2002
112views more  PAMI 2002»
13 years 7 months ago
Recognizing Handwritten Digits Using Hierarchical Products of Experts
The product of experts learning procedure [1] can discover a set of stochastic binary features that constitute a nonlinear generative model of handwritten images of digits. The qua...
Guy Mayraz, Geoffrey E. Hinton
KDD
1997
ACM
109views Data Mining» more  KDD 1997»
13 years 11 months ago
Beyond Concise and Colorful: Learning Intelligible Rules
A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful ...
Michael J. Pazzani, Subramani Mani, William Rodman...
COMAD
2009
13 years 8 months ago
Categorizing Concepts for Detecting Drifts in Stream
Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Se...
Sharanjit Kaur, Vasudha Bhatnagar, Sameep Mehta, S...
OTM
2004
Springer
14 years 23 days ago
Domain Ontology as a Resource Providing Adaptivity in eLearning
Abstract. This paper presents a knowledge-based approach to eLearning, where the domain ontology plays central role as a resource structuring the learning content and supporting ï¬...
Galia Angelova, Ognian Kalaydjiev, Albena Strupcha...
GECCO
2005
Springer
112views Optimization» more  GECCO 2005»
14 years 28 days ago
Monotonic solution concepts in coevolution
Assume a coevolutionary algorithm capable of storing and utilizing all phenotypes discovered during its operation, for as long as it operates on a problem; that is, assume an algo...
Sevan G. Ficici