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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 ...
157
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ESANN
2006
15 years 4 months ago
Neural networks and machine learning in bioinformatics - theory and applications
Bioinformatics is a promising and innovative research field. Despite of a high number of techniques specifically dedicated to bioinformatics problems as well as many successful app...
Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas...
130
Voted
ISCC
2006
IEEE
188views Communications» more  ISCC 2006»
15 years 9 months ago
Active Learning Driven Data Acquisition for Sensor Networks
Online monitoring of a physical phenomenon over a geographical area is a popular application of sensor networks. Networks representative of this class of applications are typicall...
Anish Muttreja, Anand Raghunathan, Srivaths Ravi, ...
150
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BMCBI
2010
229views more  BMCBI 2010»
15 years 3 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
CICC
2011
106views more  CICC 2011»
14 years 3 months ago
A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons
Efforts to achieve the long-standing dream of realizing scalable learning algorithms for networks of spiking neurons in silicon have been hampered by (a) the limited scalability of...
Jae-sun Seo, Bernard Brezzo, Yong Liu, Benjamin D....