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Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 28-674
Full Name: Dominic Palmer-Brown
Position: Professor
Age: ON
Sex: Male
Address: School of Computing, Information Technology and Engineering
Country: UNITED KINGDOM
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E-mail address: d.palmer-brown@uel.ac.uk
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Title of the Paper: Modal Learning Neural Networks
Authors as they appear in the Paper: Dominic Palmer-Brown, Sin Wee Lee, Chrisina Draganova, Miao Kang
Email addresses of all the authors: D.Palmer-brown@uel.ac.uk, S.W.Lee@uel.ac.uk, C.Draganova@uel.ac.uk, M.Kang@uel.ac.uk
Number of paper pages: 15
Abstract: This paper will explore the integration of learning modes into a single neural network structure in which layers of neurons or individual neurons adopt different modes. There are several reasons to explore modal learning. One motivation is to overcome the inherent limitations of any given mode (for example some modes memorise specific features, others average across features, and both approaches may be relevant according to the circumstances); another is inspiration from neuroscience, cognitive science and human learning, where it is impossible to build a serious model without consideration of multiple modes; and a third reason is non-stationary input data, or time-variant learning objectives, where the required mode is a function of time. Two modal learning ideas are presented: The Snap-Drift Neural Network (SDNN) which toggles its learning between two modes, is incorporated into an on-line system to provide carefully targeted guidance and feedback to students; an!
d an adaptive function neural network (ADFUNN), in which adaptation applies simultaneously to both the weights and the individual neuron activation functions. The combination of the two modal learning methods, in the form of Snap-drift ADaptive FUnction Neural Network (SADFUNN) is then applied to optical and pen-based recognition of handwritten digits with results that demonstrate the effectiveness of the approach.
Keywords: Modal Learning, Snap-drift, e-learning, Diagnostic Feedback, Multiple Choice Questions
EXTENSION of the file: .doc
Special (Invited) Session: Meta-Adaptation: Neurons that Change their Mode
Organizer of the Session: 607-283
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