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» Learning Functions from Imperfect Positive Data
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ICCV
2011
IEEE
12 years 10 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
PAMI
1998
87views more  PAMI 1998»
13 years 9 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
ENGL
2007
87views more  ENGL 2007»
13 years 10 months ago
Learning of Partial Languages
In this paper we introduce a finite automaton called partial finite automaton to recognize partial languages. We have defined three classes of partial languages, viz., local pa...
K. Sasikala, V. Rajkumar Dare, D. G. Thomas
EVOW
2010
Springer
14 years 1 months ago
Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments
A challenging problem in bioinformatics is the detection of residues that account for protein function specificity, not only in order to gain deeper insight in the nature of functi...
Elena Marchiori
AMAI
1998
Springer
13 years 9 months ago
Generalization and Specialization Strategies for Learning r.e. Languages
Overgeneralization is a major issue in the identification of grammars for formal languages from positive data. Different formulations of generalization and specialization strate...
Sanjay Jain, Arun Sharma