Universal Asymptotics in Committee Machines with Tree Architecture
نویسنده
چکیده
On-line supervised learning in the general K Tree Committee Machine (TCM) is studied for a uniform distribution of inputs. Examples are corrupted by noise in the teacher output. From the diierential equations which describe the learning dynamics, the algorithm which optimizes the generalization ability is exactly obtained. For a large number of presented examples, the asymptotical behaviour of the generalization error is shown to be independent of K, for nite K.
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