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» Probabilistic Neural Network Models for Sequential Data
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TNN
2008
178views more  TNN 2008»
13 years 7 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen
ICMLA
2007
13 years 9 months ago
Uncertainty optimization for robust dynamic optical flow estimation
We develop an optical flow estimation framework that focuses on motion estimation over time formulated in a Dynamic Bayesian Network. It realizes a spatiotemporal integration of ...
Volker Willert, Marc Toussaint, Julian Eggert, Edg...
KDD
2012
ACM
247views Data Mining» more  KDD 2012»
11 years 10 months ago
Integrating meta-path selection with user-guided object clustering in heterogeneous information networks
Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is its potent...
Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan...
OIR
2011
226views Neural Networks» more  OIR 2011»
12 years 10 months ago
Understanding Knowledge Sharing in Virtual Communities: An Integration of Expectancy Disconfirmation and Justice Theories
This paper integrates expectancy disconfirmation theory and justice theory to construct a model for investigating the motivations behind people’s knowledge sharing in open profe...
Chao-Min Chiu, Eric T. G. Wang, Fu-Jong Shih, Yi-W...
CIKM
2010
Springer
13 years 6 months ago
FacetCube: a framework of incorporating prior knowledge into non-negative tensor factorization
Non-negative tensor factorization (NTF) is a relatively new technique that has been successfully used to extract significant characteristics from polyadic data, such as data in s...
Yun Chi, Shenghuo Zhu