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A new optical transformation that combines geometrical coordinate transformations with the conventional optical Fourier transform is described. The resultant transformations are invariant to both scale and rotational changes in the input object or function. Extensions of these operations to optical pattern recognition and initial experimental demonstrations(More)
Application of neural nets to invariant pattern recognition is considered. The authors study various techniques for obtaining this invariance with neural net classifiers and identify the invariant-feature technique as the most suitable for current neural classifiers. A novel formulation of invariance in terms of constraints on the feature values leads to a(More)
In many practical applications, learning from imbalanced data poses a significant challenge that is increasingly faced by the machine learning community. The class imbalance problem raises issues that are either nonexistent or less severe compared to balanced class cases. This paper presents a new method for imbalanced data classification. The proposed(More)
We investigate the ability of the neocognitron to perform shift-invariant pattern recognition. Both an intuitive analysis and a more formal investigation show that the performance of the neocognitron is not intrinsically shift invariant, and that certain model parameters must be chosen appropriately to obtain approximate shift invariance. It is shown how(More)