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ML
2000
ACM
103views Machine Learning» more  ML 2000»
15 years 3 months ago
Phase Transitions in Relational Learning
One of the major limitations of relational learning is due to the complexity of verifying hypotheses on examples. In this paper we investigate this task in light of recent publishe...
Attilio Giordana, Lorenza Saitta
ML
2000
ACM
244views Machine Learning» more  ML 2000»
15 years 3 months ago
Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning
A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombinat...
Ryszard S. Michalski
GEOINFORMATICA
1998
96views more  GEOINFORMATICA 1998»
15 years 3 months ago
Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization
The nature of map generalization may be non-uniform along the length of an individual line, requiring the application of methods that adapt to the local geometry and the geographi...
Corinne Plazanet, Nara Martini Bigolin, Anne Ruas
144
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PAMI
1998
87views more  PAMI 1998»
15 years 3 months ago
Learning Local Languages and Their Application to DNA Sequence Analysis
—This paper concerns an efficient algorithm for learning in the limit a special type of regular languages called strictly locally testable languages from positive data, and its a...
Takashi Yokomori, Satoshi Kobayashi
TFS
2008
157views more  TFS 2008»
15 years 3 months ago
Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems
Abstract--This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA i...
Cheng-Jian Lin, Cheng-Hung Chen, Chin-Teng Lin