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» Hierarchical Hidden Markov Models for Information Extraction
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CP
2004
Springer
15 years 5 months ago
Leveraging the Learning Power of Examples in Automated Constraint Acquisition
Constraint programming is rapidly becoming the technology of choice for modeling and solving complex combinatorial problems. However, users of constraint programming technology nee...
Christian Bessière, Remi Coletta, Eugene C....
KDD
2002
ACM
170views Data Mining» more  KDD 2002»
16 years 1 days ago
Web site mining: a new way to spot competitors, customers and suppliers in the world wide web
When automatically extracting information from the world wide web, most established methods focus on spotting single HTMLdocuments. However, the problem of spotting complete web s...
Martin Ester, Hans-Peter Kriegel, Matthias Schuber...
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
16 years 1 days ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
SMA
2003
ACM
169views Solid Modeling» more  SMA 2003»
15 years 4 months ago
Surface reconstruction of freeform objects based on multiresolution volumetric method
3D scanners developed over the past several decades have facilitated the reconstruction of complicated engineering parts. Typically the boundary representation of a part is recons...
Sergei Azernikov, Alex Miropolsky, Anath Fischer
JCB
2008
159views more  JCB 2008»
14 years 11 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther