Neural networks : the official journal of the International Neural Network Society
Apr 6, 2018
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse bi...
Neural networks : the official journal of the International Neural Network Society
Feb 22, 2018
Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further im...
In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Per...
We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evalu...
Computational intelligence and neuroscience
Aug 17, 2016
In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minim...
Neural networks : the official journal of the International Neural Network Society
Mar 25, 2016
This article proposes a convenient tool for decoding the output of neural networks trained by Connectionist Temporal Classification (CTC) for handwritten text recognition. We use regular expressions to describe the complex structures expected in the ...
Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new mov...
The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than ...
IEEE transactions on neural networks and learning systems
Feb 26, 2015
In practical machine learning applications, human instruction is indispensable for model construction. To utilize the precious labeling effort effectively, active learning queries the user with selective sampling in an interactive way. Traditional ac...
IEEE journal of biomedical and health informatics
Apr 21, 2014
This paper presents a new approach to identify the stroke parameters in handwriting movement data understanding. A two-step analysis by synthesis paradigm is employed to facilitate the coarse-to-fine parameter identification for all strokes. One is t...
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