Neocognitron and the Map Transformation Cascade.
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.
Cardoso
Angelo
A
Department of Informatics, IST - Technical University of Lisboa, Portugal. angelo.cardoso@ist.utl.pt
Wichert
Andreas
A
D000465:Algorithms; D016000:Cluster Analysis; D003071:Cognition / Q000145:classification / Q000502:physiology*; D006236:Handwriting; D006801:Humans; D016247:Information Storage and Retrieval; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods