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NIPS
2004
15 years 5 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
NN
1998
Springer
177views Neural Networks» more  NN 1998»
15 years 3 months ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
ECCV
2006
Springer
16 years 6 months ago
Revisiting the Brightness Constraint: Probabilistic Formulation and Algorithms
Abstract. In this paper we introduce a principled approach to modeling the image brightness constraint for optical flow algorithms. Using a simple noise model, we derive a probabil...
Venu Madhav Govindu
ICPR
2004
IEEE
16 years 5 months ago
Probabilistic People Tracking for Occlusion Handling
This work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions. In particular, the tracks are described by means of probabilist...
Rita Cucchiara, Costantino Grana, Giovanni Tardini...
ICCD
2008
IEEE
139views Hardware» more  ICCD 2008»
16 years 1 months ago
Probabilistic error propagation in logic circuits using the Boolean difference calculus
- A gate level probabilistic error propagation model is presented which takes as input the Boolean function of the gate, the signal and error probabilities of the gate inputs, and ...
Nasir Mohyuddin, Ehsan Pakbaznia, Massoud Pedram