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ECAI
2004
Springer
15 years 10 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
IDEAL
2004
Springer
15 years 9 months ago
Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks
We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHM...
Jangmin O, Jae Won Lee, Sung-Bae Park, Byoung-Tak ...
HT
1998
ACM
15 years 8 months ago
Inferring Web Communities from Link Topology
The World Wide Web grows through a decentralized, almost anarchic process, and this has resulted in a large hyperlinked corpus without the kind of logical organization that can be...
David Gibson, Jon M. Kleinberg, Prabhakar Raghavan
ICCV
2009
IEEE
15 years 2 months ago
Fast realistic multi-action recognition using mined dense spatio-temporal features
Within the field of action recognition, features and descriptors are often engineered to be sparse and invariant to transformation. While sparsity makes the problem tractable, it ...
Andrew Gilbert, John Illingworth, Richard Bowden
KDD
2001
ACM
163views Data Mining» more  KDD 2001»
16 years 4 months ago
Data Mining for Typhoon Image Collection
This paper introduces the application of data mining methods to the analysis and prediction of the typhoon. The testbed for this research is the typhoon image collection that we e...
Asanobu Kitamoto