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» Learning with Weighted Transducers
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ICANN
2003
Springer
14 years 2 months ago
Optimal Hebbian Learning: A Probabilistic Point of View
Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabili...
Jean-Pascal Pfister, David Barber, Wulfram Gerstne...
CDC
2008
IEEE
142views Control Systems» more  CDC 2008»
14 years 3 months ago
Convergence of rule-of-thumb learning rules in social networks
— We study the problem of dynamic learning by a social network of agents. Each agent receives a signal about an underlying state and communicates with a subset of agents (his nei...
Daron Acemoglu, Angelia Nedic, Asuman E. Ozdaglar
GLVLSI
1998
IEEE
124views VLSI» more  GLVLSI 1998»
14 years 1 months ago
Non-Refreshing Analog Neural Storage Tailored for On-Chip Learning
In this research, we devised a new simple technique for statically holding analog weights, which does not require periodic refreshing. It further contains a mechanism to locally u...
Bassem A. Alhalabi, Qutaibah M. Malluhi, Rafic A. ...
ICPR
2010
IEEE
14 years 10 days ago
Localized Multiple Kernel Regression
Multiple kernel learning (MKL) uses a weighted combination of kernels where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kerne...
Mehmet Gönen, Ethem Alpaydin
JMLR
2012
11 years 11 months ago
UPAL: Unbiased Pool Based Active Learning
In this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothe...
Ravi Ganti, Alexander Gray