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PAKDD
2005
ACM
168views Data Mining» more  PAKDD 2005»
16 years 1 days ago
Adaptive Nonlinear Auto-Associative Modeling Through Manifold Learning
We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separatel...
Junping Zhang, Stan Z. Li
WILF
2005
Springer
194views Fuzzy Logic» more  WILF 2005»
16 years 22 hour ago
Learning Bayesian Classifiers from Gene-Expression MicroArray Data
Computing methods that allow the efficient and accurate processing of experimentally gathered data play a crucial role in biological research. The aim of this paper is to present a...
Andrea Bosin, Nicoletta Dessì, Diego Libera...
ROCAI
2004
Springer
15 years 12 months ago
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach
Abstract. In the field of Text Mining, a key phase in data preparation is concerned with the extraction of terms, i.e. collocation of words attached to specific concepts (e.g. Ph...
Mathieu Roche, Jérôme Azé, Yve...
ACMIDC
2009
15 years 10 months ago
Designing for physical-digital correspondence in tangible learning environments
In tangible learning environments the potential to exploit different physical-digital links increases representational power but also broadens the complexity of design. This paper...
Sara Price, Taciana Pontual Falcão
GECCO
2006
Springer
168views Optimization» more  GECCO 2006»
15 years 10 months ago
A Bayesian approach to learning classifier systems in uncertain environments
In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu...