Synergy of clustering multiple back propagation networks
Advances in neural information processing systems 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning decision trees from decision rules: a method and initial results from a comparative study
Journal of Intelligent Information Systems - Special issue on methodologies for intelligent systems
IEEE Computer Graphics and Applications
Democracy in neural nets: voting schemes for classification
Neural Networks
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Real world images often contain similar objects but with different rotations, noise, or other visual alterations. Vision systems should be able to recognize objects regardless of these visual alterations. This paper presents a novel approach for learning optimized structures of classifiers for recognizing visual objects regardless of certain types of visual alterations. The approach consists of two phases. The first phase is concerned with learning classifications of a set of standard and altered objects. The second phase is concerned with discovering an optimized structure of classifiers for recognizing objects from unseen images. This paper presents an application of this approach to a domain of 15 classes of hand gestures. The experimental results show significant improvement in the recognition rate rather than using a single classifier or multiple classifiers with thresholds.