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MM
2004
ACM
124views Multimedia» more  MM 2004»
16 years 21 days 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 11 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 11 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 9 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 8 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...