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ML
2002
ACM
178views Machine Learning» more  ML 2002»
14 years 9 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
ESANN
2000
14 years 11 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...
EACL
1993
ACL Anthology
14 years 11 months ago
Data-Oriented Methods for Grapheme-to-Phoneme Conversion
It is traditionally assumed that various sources of linguistic knowledge and their interaction should be formalised in order to be able to convert words into their phonemic repres...
Antal van den Bosch, Walter Daelemans
DATAMINE
2006
139views more  DATAMINE 2006»
14 years 10 months ago
Discovering Classification from Data of Multiple Sources
In many large e-commerce organizations, multiple data sources are often used to describe the same customers, thus it is important to consolidate data of multiple sources for intell...
Charles X. Ling, Qiang Yang
KBS
2006
150views more  KBS 2006»
14 years 10 months ago
Clusterer ensemble
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods ...
Zhi-Hua Zhou, Wei Tang