Matthew C. Humphrey

The University of Waikato, Hamilton, Waikato, New Zealand

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Publications (3)0 Total impact

  • M.C. Humphrey, G. Holmes, S.J. Cunningham
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    ABSTRACT: Hopfield neural networks can be used for image recognition when only a partial image is available. However, the image recognition process is very sensitive to the position of the input; shifting the image by only one pixel can cause the network to fail to find a matching exemplar. The authors present a technique for modifying the input image so that an ordinary Hopfield neural network will recognize a shifted image. This technique makes use of the image. The authors run an experiment with random bitmap images to determine how accurately a Hopfield neural network can recognize shifted and blurred images. The results indicate that the neural network can recognize shifted images only if they are modified
    Artificial Neural Networks and Expert Systems, 1993. Proceedings., First New Zealand International Two-Stream Conference on; 12/1993
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    Sally Jo Cunningham, Matthew C. Humphrey, Ian H. Witten
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    ABSTRACT: The aim of many machine learning users is to comprehend the structures that are inferred from a dataset, and such users may be far more interested in understanding the structure of their data than in predicting the outcome of new test data. Part I of this paper surveys representations based on decision trees, production rules and decision graphs that have been developed and used for machine learning. These representations have differing degrees of expressive power, and particular attention is paid to their comprehensibility for non-specialist users. The graphic form in which a structure is portrayed also has a strong effect on comprehensibility, and Part II of this paper develops knowledge visualization techniques that are particularly appropriate to help answer the questions that machine learning users typically ask about the structures produced.
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    Sally Jo Cunningham, Matthew C. Humphrey, Ian H. Witten
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    ABSTRACT: Researchers in machine learning use decision trees, production rules, and decision graphs for visualizing classification data. Part I of this paper surveyed these representations, paying particular attention to their comprehensibility for non-specialist users. Part II turns attention to knowledge visualization—the graphic form in which a structure is portrayed and its strong influence on comprehensibility. We analyze the questions that, in our experience, end users of machine learning tend to ask of the structures inferred from their empirical data. By mapping these questions onto visualization tasks, we have created new graphical representations that show the flow of examples through a decision structure. These knowledge visualization techniques are particularly appropriate in helping to answer the questions that users typically ask, and we describe their use in discovering new properties of a data set. In the case of decision trees, an automated software tool has been developed to construct the visualizations.