Forward Intergrated Feature and Architecture Selection using Neural Networks

E. Dawit and T. Sortrakul (Thailand)


: OCR, Likelihood Ratio, Feature and Architecture Selection


Forward integrated feature and architecture selections (FIFAS) is crucial in the design of classification systems in particular using neural networks algorithm for any kind of training datasets. FIFAS is easy and simple way to obtain the suitable model for a given number of features with acceptable classification accuracy rate without having trial and error methodology. It succumbs to faster, reliable and less resource usage. Furthermore, it conquers the burden of computational cost and exhaustive searching for ideal model although it may not suitable for the proponent of middle stage between accuracy and speed. The other peculiar feature of FIFAS is to permit practitioner to choice which pillar comes first for the integrated approach. This new algorithm is tested on new benchmark (Geez characters) and common available handwritten English numerals.

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