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» Experimental Evaluation of Hierarchical Hidden Markov Models
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KDD
2012
ACM
254views Data Mining» more  KDD 2012»
13 years 4 months ago
Playlist prediction via metric embedding
Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify) have fundamentally changed how music is consumed. In particular, automatically...
Shuo Chen, Josh L. Moore, Douglas Turnbull, Thorst...
SIGMOD
2003
ACM
240views Database» more  SIGMOD 2003»
16 years 2 months ago
XRANK: Ranked Keyword Search over XML Documents
We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML ...
Lin Guo, Feng Shao, Chavdar Botev, Jayavel Shanmug...
ACL
1993
15 years 3 months ago
Distributional Clustering of English Words
We describe and evaluate experimentally a method for clustering words according to their distribution in particular syntactic contexts. Words are represented by the relative frequ...
Fernando C. N. Pereira, Naftali Tishby, Lillian Le...
IJCAI
2001
15 years 3 months ago
Adaptive Control of Acyclic Progressive Processing Task Structures
The progressive processing model allows a system to trade off resource consumption against the quality of the outcome by mapping each activity to a graph of potential solution met...
Stéphane Cardon, Abdel-Illah Mouaddib, Shlo...
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BMCBI
2007
115views more  BMCBI 2007»
15 years 1 months ago
A novel, fast, HMM-with-Duration implementation - for application with a new, pattern recognition informed, nanopore detector
Background: Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) ...
Stephen Winters-Hilt, Carl Baribault