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165
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NIPS
1992
15 years 4 months ago
A Note on Learning Vector Quantization
Vector Quantization is useful for data compression. Competitive Learning which minimizes reconstruction error is an appropriate algorithm for vector quantization of unlabelled dat...
Virginia R. de Sa, Dana H. Ballard
113
Voted
AUTOMATICA
2007
90views more  AUTOMATICA 2007»
15 years 3 months ago
Stability analysis of discrete-time iterative learning control systems with interval uncertainty
This paper presents a stability analysis of the iterative learning control (ILC) problem for discrete-time systems when the plant Markov parameters are subject to interval uncerta...
Hyo-Sung Ahn, Kevin L. Moore, Yangquan Chen
115
Voted
JAIR
2006
111views more  JAIR 2006»
15 years 3 months ago
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning prob...
Vadim Bulitko, Greg Lee
96
Voted
TNN
2008
82views more  TNN 2008»
15 years 3 months ago
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks
In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find ...
Cristiano Cervellera, Danilo Macciò, Marco ...
152
Voted
AROBOTS
2002
91views more  AROBOTS 2002»
15 years 3 months ago
Fast, On-Line Learning of Globally Consistent Maps
To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot ...
Tom Duckett, Stephen Marsland, Jonathan Shapiro