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Kang, Miao; Palmer-Brown, Dominic (2008)
Languages: English
Types: Unknown

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ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
An ADaptive Function Neural Network (ADFUNN) is combined with the on-line snapdrift learning method in this paper to perform optical and pen-based recognition of handwritten digits. Snap-Drift employs the complementary concepts of minimalist common feature learning (snap) and vector quantization (drift towards the input patterns), and is a fast unsupervised method suitable for real-time learning and non-stationary environments where new patterns are continually introduced. The ADaptive FUction Neural Network (ADFUNN) is based on a linear piecewise neuron activation function that is modified by a gradient descent supervised learning algorithm. It has previously been applied to the Iris dataset, and a natural language phrase recognition problem, exhibiting impressive generalisation classification ability without the hidden neurons that are usually required for linearly inseparable data. The unsupervised single layer Snap-Drift is effective in extracting distinct features from the complex cursive-letter datasets, and the supervised single layer ADFUNN is capable of solving linearly inseparable problems rapidly. In combination within one network (SADFUNN), these two methods are more powerful and yet simpler than MLPs (a standard neural network), at least on this problem domain. The optical and pen-based handwritten digits data are from UCI machine learning repository. The classifications are learned rapidly and produce higher generalisation results than a MLP with standard learning methods.
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    Title Year Similarity

    Modal Learning Neural Networks


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