Proceedings of the NATO Advanced Research Workshop on Neural computers
Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
An introduction to neural computing
An introduction to neural computing
Local overfitting control via leverages
Neural Computation
Mental Imagery in Explanations of Visual Object Classification
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Fuzzy Boolean Networks Learning Behaviour
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
Weightless Neural Networks: Knowledge-Based Inference System
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Pattern recognition and reading by machine
IRE-AIEE-ACM '59 (Eastern) Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Hi-index | 0.01 |
The WiSARD (Wilkie, Stonham and Aleksander's Recognition Device) weightless neural network model has its functionality based on the collective response of RAM-based neurons. WiSARD's learning phase consists on writing at the RAM neurons' positions addressed (typically through a pseudo-random mapping) by binary training patterns. By counting the frequency of writing accesses at RAM neuron positions during the learning phase, it is possible to associate the most accessed addresses with the corresponding input field contents that defined them. The idea of associating this process with the formation of ''mental'' images is explored in the DRASiW model, a WiSARD extension provided with the ability of producing pattern examples, or prototypes, derived from learnt categories. This work demonstrates the equivalence of two ways of generating such prototypes: (i) via frequency counting and filtering and (ii) via formulating fuzzy rules. Moreover, it is shown, through the exploration of the MNIST database of handwritten digits as benchmark, how the process of mental images formation can improve WiSARD's classification skills.