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» Active Hidden Markov Models for Information Extraction
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JMLR
2008
188views more  JMLR 2008»
14 years 9 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
ISMIR
2005
Springer
170views Music» more  ISMIR 2005»
15 years 3 months ago
Applications of Binary Classification and Adaptive Boosting to the Query-By-Humming Problem
In the “query-by-humming” problem, we attempt to retrieve a specific song from a target set based on a sung query. Recent evaluations of query-by-humming systems show that th...
Charles L. Parker
ISMIR
2005
Springer
151views Music» more  ISMIR 2005»
15 years 3 months ago
Markov Random Fields and Maximum Entropy Modeling for Music Information Retrieval
Music information retrieval is characterized by a number of various user information needs. Systems are being developed that allow searchers to find melodies, rhythms, genres, an...
Jeremy Pickens, Costas S. Iliopoulos
STDBM
2004
Springer
171views Database» more  STDBM 2004»
15 years 3 months ago
Extracting Mobility Statistics from Indexed Spatio-Temporal Datasets
With the recent progress of spatial information technologies and mobile computing technologies, spatio-temporal databases which store information on moving objects including vehicl...
Yoshiharu Ishikawa, Yuichi Tsukamoto, Hiroyuki Kit...
ICDM
2006
IEEE
164views Data Mining» more  ICDM 2006»
15 years 3 months ago
Unsupervised Learning of Tree Alignment Models for Information Extraction
We propose an algorithm for extracting fields from HTML search results. The output of the algorithm is a database table– a data structure that better lends itself to high-level...
Philip Zigoris, Damian Eads, Yi Zhang