An optimal weight learning machine for handwritten digit image recognition

An optimal weight learning machine for a single-hidden layer feedforward network (SLFN) with the application to handwritten digit image recognition is developed in this paper. It is seen that both the input weights and the output weights of the SLFN are globally optimized with the batch learning type of least squares. All feature vectors of the classifier can then be placed at the prescribed positions in the feature space in the sense that the separability of all nonlinearly separable patterns can be maximized, and a high degree of recognition accuracy can be achieved with a small number of hidden nodes in the SLFN. An experiment for the recognition of the handwritten digit image from both the MNIST database and the USPS database is performed to show the excellent performance and effectiveness of the proposed methodology.

► We develop an optimal weight learning machine for handwritten digit image recognition.
► Both the input and output weights of the SLFN are globally optimized with batch learning.
► All feature vectors of the classifier can be placed at the prescribed positions in feature space.
► The separability of all nonlinearly separable patterns can be maximized.
► A high degree of recognition accuracy can be achieved with a small number of hidden nodes.