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» Approximation Methods for Supervised Learning
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WSOM
2009
Springer
14 years 4 months ago
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ
Vector quantization methods are confronted with a model selection problem, namely the number of prototypical feature representatives to model each class. In this paper we present a...
Alexander Denecke, Heiko Wersing, Jochen J. Steil,...
ICDM
2006
IEEE
152views Data Mining» more  ICDM 2006»
14 years 4 months ago
Application of Graph-based Data Mining to Metabolic Pathways
We present a method for finding biologically meaningful patterns on metabolic pathways using the SUBDUE graph-based relational learning system. A huge amount of biological data t...
Chang Hun You, Lawrence B. Holder, Diane J. Cook
DAGM
2008
Springer
13 years 11 months ago
Approximate Parameter Learning in Conditional Random Fields: An Empirical Investigation
We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the paramet...
Filip Korc, Wolfgang Förstner
ICML
2000
IEEE
14 years 10 months ago
On-line Learning for Humanoid Robot Systems
Humanoid robots are high-dimensional movement systems for which analytical system identification and control methods are insufficient due to unknown nonlinearities in the system s...
Gaurav Tevatia, Jörg Conradt, Sethu Vijayakum...
DELTA
2008
IEEE
13 years 11 months ago
A Spiking Neural Network for Gas Discrimination Using a Tin Oxide Sensor Array
We propose a bio-inspired signal processing method for odor discrimination. A spiking neural network is trained with a supervised learning rule so as to classify the analog outputs...
Maxime Ambard, Bin Guo, Dominique Martinez, Amine ...