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MM
2004
ACM
124views Multimedia» more  MM 2004»
15 years 10 months ago
An online-optimized incremental learning framework for video semantic classification
This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic c...
Jun Wu, Xian-Sheng Hua, HongJiang Zhang, Bo Zhang
RT
2001
Springer
15 years 9 months ago
Decoupling Strokes and High-Level Attributes for Interactive Traditional Drawing
We present an interactive system, which allows the user to produce drawings in a variety of traditional styles. It takes as input an image and performs semi-automatic tonal modelin...
Frédo Durand, Victor Ostromoukhov, Mathieu ...
NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 9 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
WSC
2008
15 years 7 months ago
Using simulation with Design For Six Sigma in a server manufacturing environment
This research presents an integrated simulation modelingDesign For Six Sigma (DFSS) framework to study the design and process issues in a server manufacturing environment. The ser...
Sreekanth Ramakrishnan, Pei-Fang Tsai, Christiana ...
NIPS
2008
15 years 6 months ago
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...