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NLPRS
2001
Springer
15 years 11 months ago
A Maximum Entropy Tagger with Unsupervised Hidden Markov Models
We describe a new tagging model where the states of a hidden Markov model (HMM) estimated by unsupervised learning are incorporated as the features in a maximum entropy model. Our...
Jun'ichi Kazama, Yusuke Miyao, Jun-ichi Tsujii
AIIA
1995
Springer
15 years 10 months ago
Learning Programs in Different Paradigms using Genetic Programming
Genetic Programming (GP) is a method of automatically inducing programs by representing them as parse trees. In theory, programs in any computer languages can be translated to par...
Man Leung Wong, Kwong-Sak Leung
ECML
2003
Springer
15 years 11 months ago
Learning Context Free Grammars in the Limit Aided by the Sample Distribution
We present an algorithm for learning context free grammars from positive structural examples (unlabeled parse trees). The algorithm receives a parameter in the form of a finite se...
Yoav Seginer
ICIP
2003
IEEE
16 years 8 months ago
MRA data segmentation using level sets
In this paper, we use a level set based segmentation algorithm to extract the vascular tree from Magnetic Resonance Angiography, "MRA". Classification model finds an opt...
Hossam S. Hassan, Aly A. Farag
ICPR
2008
IEEE
16 years 29 days ago
A symbol graph based handwritten math expression recognition
In online handwritten math expression recognition, one-pass dynamic programming can produce highquality symbol graphs in addition to best symbol sequence hypotheses [3], especiall...
Yu Shi, Frank K. Soong