Deep machine learning has received significant attention over the past decade, especially in terms of dealing with information that may span large scales. By employing a hierarchical architecture, consisting of simple computational nodes of similar characteristic, such a network helps to partition large data structures into relatively smaller, more manageable units, and to discover any dependencies that may exist between the resulting units. However, the process of running this type of network which has a layered structure, to perform tasks such as feature extraction, and subsequent feature pattern-based recognition, typically involves significant computation. To tackle this problem, two approaches are proposed in this thesis. The first novel approach developed is for image classification, by integrating deep learning and feature interpolation, supported with advanced learning classification techniques. The recently introduced Deep Spatio-Temporal Inference Network (DeSTIN) is employed to carry out limited original feature extraction. Simple interpolation is then employed to artificially increase the dimensionality of the extracted feature sets for accurate classification, without incurring heavy computational cost. The work is tested against the popular MNIST dataset of handwritten digits, demonstrating the potential of the proposed work. The second approach, which is a substantially simplified 2-layer learning network, is introduced that exploits unsupervised learning for pattern representation, capable of extracting effective features efficiently. Experimental results, in comparison with the use of popular deep learning networks, again on the application to handwritten digit classification demonstrate that the proposed approach is of significant potential in dealing with real-world problems. The generation of effective feature pattern-based classification rules from data is essential to the development of intelligent classifiers which are readily comprehensible to the user. Unfortunately, a sparse rule base may be generated when there is missing information in the experienced dataset. This hinders classification systems that work based on such sparse knowledge effectively performing their tasks in many real-world applications, where complete historical data cannot be assumed. This thesis further proposes an innovative approach by integrating fuzzy rule interpolation within a data-driven classification mechanism, such that conclusions can be approximately derived even if no matched rule can be found from a given sparse rule base when given a certain observation. The proposed technique is simple conceptually, directly exploiting the recently developed fuzzy rule interpolation techniques. However, the resulting integrated system offers a powerful means to develop robust classifiers, significantly enhancing the effectiveness of intelligent classification systems, as demonstrated by systematic comparative experimental results and also, by an application to the challenging problem of mammographic risk analysis.
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Thesis, 4 MB, PDF
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