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» Learning to generalize for complex selection tasks
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CEC
2005
IEEE
13 years 9 months ago
Co-evolutionary modular neural networks for automatic problem decomposition
Abstract- Decomposing a complex computational problem into sub-problems, which are computationally simpler to solve individually and which can be combined to produce a solution to ...
Vineet R. Khare, Xin Yao, Bernhard Sendhoff, Yaoch...
IJCNN
2000
IEEE
14 years 3 days ago
Supervised Scaled Regression Clustering: An Alternative to Neural Networks
: This paper describes a rather novel method for the supervised training of regression systems that can be an alternative to feedforward Artificial Neural Networks (ANNs) trained w...
Mark J. Embrechts, Dirk Devogelaere, Marcel Rijcka...
IROS
2006
IEEE
127views Robotics» more  IROS 2006»
14 years 1 months ago
Learning Predictive Features in Affordance based Robotic Perception Systems
This work is about the relevance of Gibson’s concept of affordances [1] for visual perception in interactive and autonomous robotic systems. In extension to existing functional ...
Gerald Fritz, Lucas Paletta, Ralph Breithaupt, Eri...
CORR
2012
Springer
214views Education» more  CORR 2012»
12 years 3 months ago
Sum-Product Networks: A New Deep Architecture
The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are the most general conditions under...
Hoifung Poon, Pedro Domingos
CSL
1998
Springer
13 years 7 months ago
Evaluating spoken dialogue agents with PARADISE: Two case studies
This paper presents PARADISE PARAdigm for DIalogue System Evaluation, a general framework for evaluating and comparing the performance of spoken dialogue agents. The framework d...
Marilyn A. Walker, Diane J. Litman, Candace A. Kam...