Abstract
In this paper, we proposed a ‘Projection Based Learning for Meta-cognitive Radial Basis Function Network (PBL-McRBFN)’ classifier for effective diagnosis of Parkinson’s disease. McRBFN is inspired by human meta-cognitive learning principles. McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, network parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. The experimental results on parkinson’s data sets based on vocal and gait features clearly highlight the superior performance of PBL-McRBFN classifier over results reported in the literature for detection of individual with or without PD.
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Sateesh Babu, G., Suresh, S., Uma Sangumathi, K., Kim, H.J. (2012). A Projection Based Learning Meta-cognitive RBF Network Classifier for Effective Diagnosis of Parkinson’s Disease. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_67
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DOI: https://doi.org/10.1007/978-3-642-31362-2_67
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