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» Activity Modeling Using Event Probability Sequences
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NAR
2006
164views more  NAR 2006»
13 years 7 months ago
FISH - family identification of sequence homologues using structure anchored hidden Markov models
The FISH server is highly accurate in identifying the family membership of domains in a query protein sequence, even in the case of very low sequence identities to known homologue...
Jeanette Tångrot, Lixiao Wang, Bo Kågs...
ICPR
2008
IEEE
14 years 2 months ago
Anomalous trajectory patterns detection
In the field of event analysis, the detection of anomalous events has often been based on the creation of a model representing the most common patterns of activity detected withi...
Claudio Piciarelli, Christian Micheloni, Gian Luca...
EMNLP
2004
13 years 9 months ago
Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech
We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and max...
Yang Liu, Andreas Stolcke, Elizabeth Shriberg, Mar...
KDD
2009
ACM
169views Data Mining» more  KDD 2009»
14 years 2 months ago
On burstiness-aware search for document sequences
As the number and size of large timestamped collections (e.g. sequences of digitized newspapers, periodicals, blogs) increase, the problem of efficiently indexing and searching su...
Theodoros Lappas, Benjamin Arai, Manolis Platakis,...
MM
2003
ACM
161views Multimedia» more  MM 2003»
14 years 27 days ago
Affective content detection using HMMs
This paper discusses a new technique for detecting affective events using Hidden Markov Models(HMM). To map low level features of video data to high level emotional events, we per...
Hang-Bong Kang