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NIPS
1998
15 years 7 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
NIPS
1989
15 years 7 months ago
The Cascade-Correlation Learning Architecture
Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology,...
Scott E. Fahlman, Christian Lebiere
JCNS
2000
78views more  JCNS 2000»
15 years 6 months ago
Computational Consequences of Temporally Asymmetric Learning Rules: II. Sensory Image Cancellation
Abstract. The electrosensory lateral line lobe (ELL) of mormyridelectric sh is a cerebellum-likestructure that receives primarya erent input from electroreceptors in the skin. Purk...
Patrick D. Roberts, Curtis C. Bell
AIME
2009
Springer
15 years 3 months ago
Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach
Lung cancer represents the most deadly type of malignancy. In this work we propose a machine learning approach to segmenting lung tumours in Positron Emission Tomography (PET) scan...
Aliaksei Kerhet, Cormac Small, Harvey Quon, Terenc...
ANOR
2011
175views more  ANOR 2011»
15 years 1 months ago
Integrated exact, hybrid and metaheuristic learning methods for confidentiality protection
A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentialit...
Fred Glover, Lawrence H. Cox, Rahul Patil, James P...