A simple and practical review of over-fitting in neural network learning Online publication date: Mon, 22-Jan-2018
by Oyebade K. Oyedotun; Ebenezer O. Olaniyi; Adnan Khashman
International Journal of Applied Pattern Recognition (IJAPR), Vol. 4, No. 4, 2017
Abstract: Training a neural network involves the adaptation of its internal parameters for modelling a specific task. The states of the internal parameters during training describe how much experiential knowledge the model has acquired. Although, it is desirable that a trained neural network achieves zero classification error on the training examples while tuning its internal parameters for a task, the amount of generalisation power that is lost while enforcing such a learning constraint on the model is quite important. In this paper, we review from a practical perspective the consequences of enforcing such a learning constraint which results in a model that has learned a smooth mapping function or essentially 'memorised' the training data. In addition, we investigate how the curse of dimensionality relates to such a learning constraint. For our experiments, we consider handwritten character recognition applications using publicly available datasets.
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