Blackmore, Kim Louise
A nonlinear generalisation of the perceptron learning algorithm is presented and analysed.
The new algorithm is designed for learning nonlinearly parametrised decision
regions. It is shown that this algorithm can be viewed as a stepwise gradient descent
of a certain cost function. Averaging theory is used to describe the behaviour of the
algorithm, and in the process conditions guaranteeing convergence of the algorithm are
established. These conditions are hard to test, so some simpler...[Show more]
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