title: A convolutional recursive modified Self Organizing Map for handwritten digits recognition.
abstract: It is well known that the handwritten digits recognition is a challenging problem. Different classification algorithms have been applied to solve it. Among them, the Self Organizing Maps (SOM) produced promising results. In this paper, first we introduce a Modified SOM for the vector quantization problem with improved initialization process and topology preservation. Then we develop a Convolutional Recursive Modified SOM and apply it to the problem of handwritten digits recognition. The computational results obtained using the well known MNIST dataset demonstrate the superiority of the proposed algorithm over the existing SOM-based algorithms.
authors.affiliation: Faculty of Science and Technology, Federation University Australia, Victoria, 3353, Australia. Electronic address: eh.mohebi@gmail.com.
authors.forename: Ehsan
authors.initials: E
authors.lastname: Mohebi
authors.affiliation: Faculty of Science and Technology, Federation University Australia, Victoria, 3353, Australia. Electronic address: a.bagirov@federation.edu.au.
authors.forename: Adil
authors.initials: A
authors.lastname: Bagirov
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D006236:Handwriting; D016571:Neural Networks, Computer
keywords: Convolutional neural network; Recursive neural network; SOM initialization; SOM topology; Self Organizing Maps; Vector quantization