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108
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ESANN
2003
15 years 2 months ago
Approximation of Function by Adaptively Growing Radial Basis Function Neural Networks
In this paper a neural network for approximating function is described. The activation functions of the hidden nodes are the Radial Basis Functions (RBF) whose parameters are learn...
Jianyu Li, Siwei Luo, Yingjian Qi
117
Voted
IJCAI
2003
15 years 1 months ago
Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking
This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tr...
Yang Wang 0002, Tele Tan, Kia-Fock Loe
NIPS
1998
15 years 1 months ago
Dynamically Adapting Kernels in Support Vector Machines
The kernel-parameter is one of the few tunable parameters in Support Vector machines, controlling the complexity of the resulting hypothesis. Its choice amounts to model selection...
Nello Cristianini, Colin Campbell, John Shawe-Tayl...
94
Voted
NIPS
1993
15 years 1 months ago
Computational Elements of the Adaptive Controller of the Human Arm
We consider the problem of how the CNS learns to control dynamics of a mechanical system. By using a paradigm where a subject's hand interacts with a virtual mechanical envir...
Reza Shadmehr, Ferdinando A. Mussa-Ivaldi
89
Voted
NIPS
1996
15 years 1 months ago
Adaptively Growing Hierarchical Mixtures of Experts
We propose a novelapproach to automaticallygrowing and pruning Hierarchical Mixtures of Experts. The constructive algorithm proposed here enables large hierarchies consisting of s...
Jürgen Fritsch, Michael Finke, Alex Waibel