Shift invariance and the neocognitron
Neural Networks
Computer
Recognition of handwritten digits in the real world by a neocognitron
Knowledge-based intelligent techniques in character recognition
Self-organization of shift-invariant receptive fields
Neural Networks
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Exploratory analysis of event-related fMRI demonstrated in a working memory study
Exploratory analysis and data modeling in functional neuroimaging
Handwritten alphanumeric character recognition by the neocognitron
IEEE Transactions on Neural Networks
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Based on our observations of the working principles of the archetypal hierarchical neural network, Neocognitron, we propose a simplified model which we call the Map Transformation Cascade. The least complex Map Transformation Cascade can be understood as a sequence of filters, which maps and transforms the input pattern into a space where patterns in the same class are close. The output of the filters is then passed to a simple classifier, which yields a classification for the input pattern. Instead of a specifically crafted learning algorithm, the Map Transformation Cascade separates two different learning needs: Information reduction, where a clustering algorithm is more suitable (e.g., K-Means) and classification, where a supervised classifier is more suitable (e.g., nearest neighbor method). The performance of the proposed model is analyzed in handwriting recognition. The Map Transformation Cascade achieved performance similar to that of Neocognitron.