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» An empirical comparison of supervised learning algorithms
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ESANN
2000
15 years 1 months ago
SpikeProp: backpropagation for networks of spiking neurons
Abstract. For a network of spiking neurons with reasonable postsynaptic potentials, we derive a supervised learning rule akin to traditional error-back-propagation, SpikeProp and s...
Sander M. Bohte, Joost N. Kok, Johannes A. La Pout...
ESANN
2001
15 years 1 months ago
Learning fault-tolerance in Radial Basis Function Networks
This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
Xavier Parra, Andreu Català
TSMC
2002
102views more  TSMC 2002»
14 years 11 months ago
Generalized pursuit learning schemes: new families of continuous and discretized learning automata
The fastest learning automata (LA) algorithms currently available fall in the family of estimator algorithms introduced by Thathachar and Sastry [24]. The pioneering work of these ...
M. Agache, B. John Oommen
ECML
2003
Springer
15 years 4 months ago
Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language
Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algor...
Walter Daelemans, Véronique Hoste, Fien De ...

Publication
400views
14 years 6 months ago
Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting
For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing...
Hui Kong, Metin Gurcan, and Kamel Belkacem-Boussai...