A neural model for multi-expert architectures
<|reference_start|>A neural model for multi-expert architectures: We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives.<|reference_end|>
@article{toussaint2002a,
    title={A neural model for multi-expert architectures},
    author={Marc Toussaint},
    journal={Proceedings of the International Joint Conference on Neural
  Networks (IJCNN 2002), 2755-2760.},
    year={2002},
    archivePrefix={arXiv},
    eprint={nlin/0202039},
    primaryClass={nlin.AO cond-mat.dis-nn cs.NE}
}