Two-Stage Feature Generator for Handwritten Digit Classification
M. Gunler Pirim,
Hakan Tora,
Kasim Oztoprak
et al.
Abstract:In this paper, a novel feature generator framework is proposed for handwritten digit classification. The proposed framework includes a two-stage cascaded feature generator. The first stage is based on principal component analysis (PCA), which generates projected data on principal components as features. The second one is constructed by a partially trained neural network (PTNN), which uses projected data as inputs and generates hidden layer outputs as features. The features obtained from the PCA and PTNN-based … Show more
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