Adaptive Elastic Input Field for Recognition Improvement

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Minoru Asogawa


For machines to perform classification tasks, such as speech and character recognition, appropriately handling deformed patterns is a key to achieving high performance. The authors presents a new type of classification system, an Adaptive Input Field Neu(cid:173) ral Network (AIFNN), which includes a simple pre-trained neural network and an elastic input field attached to an input layer. By using an iterative method, AIFNN can determine an optimal affine translation for an elastic input field to compensate for the original deformations. The convergence of the AIFNN algorithm is shown. AIFNN is applied for handwritten numerals recognition. Conse(cid:173) quently, 10.83% of originally misclassified patterns are correctly categorized and total performance is improved, without modifying the neural network.