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IFIP12
2008
13 years 11 months ago
Bayesian Networks Optimization Based on Induction Learning Techniques
Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learni...
Paola Britos, Pablo Felgaer, Ramón Garc&iac...
IROS
2006
IEEE
119views Robotics» more  IROS 2006»
14 years 4 months ago
Learning Similar Tasks From Observation and Practice
— This paper presents a case study of learning to select behavioral primitives and generate subgoals from observation and practice. Our approach uses local features to generalize...
Darrin C. Bentivegna, Christopher G. Atkeson, Gord...
INTERSPEECH
2010
13 years 4 months ago
Learning a language model from continuous speech
This paper presents a new approach to language model construction, learning a language model not from text, but directly from continuous speech. A phoneme lattice is created using...
Graham Neubig, Masato Mimura, Shinsuke Mori, Tatsu...
AII
1992
14 years 2 months ago
Learning from Multiple Sources of Inaccurate Data
Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies ...
Ganesh Baliga, Sanjay Jain, Arun Sharma
SYNASC
2006
IEEE
211views Algorithms» more  SYNASC 2006»
14 years 4 months ago
HTML Pattern Generator--Automatic Data Extraction from Web Pages
Existing methods of information extraction from HTML documents include manual approach, supervised learning and automatic techniques. The manual method has high precision and reca...
Mirel Cosulschi, Adrian Giurca, Bogdan Udrescu, Ni...