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» Computing Vector Functions on Anonymous Networks
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FOCI
2007
IEEE
14 years 2 months ago
Realized through a Marriage with Modular-Networks
Abstract— This paper presents a new development of selforganizing maps (SOM), realized by combining them with the idea of a modular network. This we called a modular network SOM ...
Tetsuo Furukawa, Kazuhiro Tokunaga
ICPR
2008
IEEE
14 years 9 months ago
Pattern rejection strategies for the design of self-paced EEG-based Brain-Computer Interfaces
This paper deals with pattern rejection strategies for self-paced Brain-Computer Interfaces (BCI). First, it introduces two pattern rejection strategies not used yet for self-pace...
Fabien Lotte, Harold Mouchère, Anatole L&ea...
ICASSP
2011
IEEE
13 years 9 days ago
Arccosine kernels: Acoustic modeling with infinite neural networks
Neural networks are a useful alternative to Gaussian mixture models for acoustic modeling; however, training multilayer networks involves a difficult, nonconvex optimization that...
Chih-Chieh Cheng, Brian Kingsbury
ICPR
2000
IEEE
14 years 9 months ago
General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of D
An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error in [3]. The bias/vari...
Jakob Vogdrup Hansen, Tom Heskes
IPSN
2005
Springer
14 years 2 months ago
Sensing capacity for discrete sensor network applications
We bound the number of sensors required to achieve a desired level of sensing accuracy in a discrete sensor network application (e.g. distributed detection). We model the state of...
Yaron Rachlin, Rohit Negi, Pradeep K. Khosla