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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Miao Kang Palmer-Brown, D. |
| Copyright Year | 2007 |
| Description | Author affiliation: East London Univ., London (Miao Kang; Palmer-Brown, D.) |
| Abstract | The letter image recognition dataset from UCI repository provides a complex pattern recognition problem which is to classify distorted raster images of English alphabetic characters. ADFUNN, the ANN deployed for this problem, is based on a linear piecewise neuron activation function that is modified by a novel gradient descent supervised learning algorithm. Linearly inseparable problems can be solved by ADFUNN, whereas the traditional single-layer perceptron (SLP) is incapable of solving them without a hidden layer. Multi-layer ADFUNNs (MADFUNNs) are used for the UCI distorted character recognition task. We construct a system with two parts, letter feature grouping and letter classification, to cope with the complexity of the wide diversity among the different fonts and attributes. Testing on 4,000 randomly selected test data, with all occurrences of the 16,000 training patterns removed, yields 87.6% (pure) generalisation. Allowing for naturally occurring instances of training data within the test data, yields 93.77% (natural) generalisation. |
| Starting Page | 2817 |
| Ending Page | 2822 |
| File Size | 681796 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4371406 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Neural networks Multi-layer neural network Image recognition Testing Pattern recognition Artificial neural networks Neurons Supervised learning Character recognition |
| Content Type | Text |
| Resource Type | Article |
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