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» Learning with Neural Networks in the Domain of Graphs
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AR
2007
105views more  AR 2007»
14 years 9 months ago
Reinforcement learning of a continuous motor sequence with hidden states
—Reinforcement learning is the scheme for unsupervised learning in which robots are expected to acquire behavior skills through self-explorations based on reward signals. There a...
Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sug...
ACMICEC
2008
ACM
270views ECommerce» more  ACMICEC 2008»
14 years 11 months ago
Adaptive strategies for predicting bidding prices in supply chain management
Supply Chain Management (SCM) involves a number of interrelated activities from negotiating with suppliers to competing for customer orders and scheduling the manufacturing proces...
Yevgeniya Kovalchuk, Maria Fasli
ICANNGA
2009
Springer
212views Algorithms» more  ICANNGA 2009»
15 years 4 months ago
Evolutionary Regression Modeling with Active Learning: An Application to Rainfall Runoff Modeling
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
Ivo Couckuyt, Dirk Gorissen, Hamed Rouhani, Eric L...
68
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KDD
2005
ACM
130views Data Mining» more  KDD 2005»
15 years 10 months ago
Simple and effective visual models for gene expression cancer diagnostics
In the paper we show that diagnostic classes in cancer gene expression data sets, which most often include thousands of features (genes), may be effectively separated with simple ...
Gregor Leban, Minca Mramor, Ivan Bratko, Blaz Zupa...
ESANN
2007
14 years 11 months ago
How to process uncertainty in machine learning?
Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
Barbara Hammer, Thomas Villmann