A projection based learning meta-cognitive RBF network classifier for effective diagnosis of Parkinson's disease

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7368 LNCS, Issue PART 2, 2012, Pages 611-620

  Sateesh Babu, G ^a   Suresh, S ^a   Uma Sangumathi, K ^a   Kim, H J ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b Korea University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS LABELS; DATA SETS; DECISION SURFACES; ENERGY FUNCTIONS; GAIT FEATURES; HIDDEN NEURONS; METACOGNITIVES; NETWORK CLASSIFIERS; NETWORK PARAMETERS; PARKINSON'S DISEASE;

NEURAL NETWORKS; NEURODEGENERATIVE DISEASES; NEURONS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 84865109404     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31362-2_67     Document Type: Conference Paper

References (18)

1. Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J., Ramig, L.O.: Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease. IEEE Transactions on Biomedical Engineering 56(4), 1015-1022 (2009)
2. Strom, F., Koker, R.: A parallel neural network approach to prediction of Parkinson's Disease. Expert Systems with Applications 38(10), 12470-12474 (2011)
3. Das, R.: A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications 37(2), 1568-1572 (2010)
4. Tahir, N.M.,Manap, H.H.: Parkinson Disease Gait Classification based on Machine Learning Approach. Journal of Applied Sciences 12(2), 180-185 (2012)
5. Jeon, H.S., Han, J., Yi,W.J., Jeon, B., Park, K.S.: Classification of Parkinson gait and normal gait using Spatial-Temporal Image of Plantar pressure. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4672-4675 (2008)
6. Wenden, A.L.: Metacognitive knowledge and language learning. Applied Linguistics 19(4), 515-537 (1998)
7. Rivers, W.P.: Autonomy at All costs: An Ethnography of Metacognitive Self-Assessment and Self-Management among Experienced Language Learners. The Modern Language Journal 85(2), 279-290 (2001)
8. Isaacson, R., Fujita, F.: Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning. Journal of the Scholarship of Teaching and Learning 6(1), 39-55 (2006)
9. Suresh, S., Dong, K., Kim, H.J.: A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16-18), 3012-3019 (2010)
10. Suresh, S., Savitha, R., Sundararajan, N.: A Sequential Learning Algorithm for Complex-valued Self-regulating Resource Allocation Network-CSRAN. IEEE Transactions on Neural Networks 22(7), 1061-1072 (2011)
11. Sateesh Babu, G., Suresh, S.: Meta-cognitive Neural Network for classification problems in a sequential learning framework. Neurocomputing 81, 86-96 (2012)
12. Savitha, R., Suresh, S., Sundararajan, N.: Metacognitive learning in a Fully Complex-valued Radial Basis Function Neural Network. Neural Computation 24(5), 1297-1328 (2012)
13. Suresh, S., Subramanian, K.: A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems. In: The International Joint Conference on Neural Networks (IJCNN), pp. 2507-2512 (2011)
14. Suresh, S., Sundararajan, N., Saratchandran, P.: Risk-sensitive loss functions for sparse multi-category classification problems. Information Sciences 178(12), 2621-2638 (2008)
15. Hausdorff, J.M., Lowenthal, J., Herman, T., Gruendlinger, L., Peretz, C., Giladi, N.: Rhythmic auditory stimulation modulates gait variability in Parkinson's disease. European Journal of Neuroscience 26(8), 2369-2375 (2007)
16. Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(2), 513-529 (2012)
17. Suresh, S., Sundararajan, N., Saratchandran, P.: A sequential multi-category classifier using radial basis function networks. Neurocomputing 71(7-9), 1345-1358 (2008)
18. Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1-27:27 (2011) software available at, http://www.csie.ntu.edu.tw/~cjlin/libsvm