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» Learning and Classification of Complex Dynamics
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ISCAS
2006
IEEE
144views Hardware» more  ISCAS 2006»
14 years 1 months ago
A VLSI spike-driven dynamic synapse which learns only when necessary
— We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a network of integrate-and-fire neurons. This biologically inspired synapse is hi...
S. Mitra, Stefano Fusi, Giacomo Indiveri
ICANN
2010
Springer
13 years 8 months ago
Reinforcement Learning Based Neural Controllers for Dynamic Processes without Exploration
Abstract. In this paper we present a Reinforcement Learning (RL) approach with the capability to train neural adaptive controllers for complex control problems without expensive on...
Frank-Florian Steege, André Hartmann, Erik ...
ETS
2009
IEEE
98views Hardware» more  ETS 2009»
13 years 5 months ago
Increasing Robustness of SAT-based Delay Test Generation Using Efficient Dynamic Learning Techniques
Due to the increased speed in modern designs, testing for delay faults has become an important issue in the postproduction test of manufactured chips. A high fault coverage is nee...
Stephan Eggersglüß, Rolf Drechsler
ICMLA
2008
13 years 9 months ago
Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
Jun Won Lee, Christophe G. Giraud-Carrier
AVSS
2006
IEEE
13 years 11 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks