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Fuzzy Neuro Systems for Machine Learning for Large Data Sets

Fuzzy Neuro Systems for Machine Learning for Large Data Sets. Rahul Kala, Department of Information Technology Indian Institute of Information Technology and Management Gwalior http://students.iiitm.ac.in/~ipg_200545/ rahulkalaiiitm@yahoo.co.in, rkala@students.iiitm.ac.in.

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Fuzzy Neuro Systems for Machine Learning for Large Data Sets

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  1. Fuzzy Neuro Systems for Machine Learning for Large Data Sets Rahul Kala, Department of Information Technology Indian Institute of Information Technology and Management Gwalior http://students.iiitm.ac.in/~ipg_200545/ rahulkalaiiitm@yahoo.co.in, rkala@students.iiitm.ac.in Paper: Kala, Rahul; Shukla, Anupam; Tiwari, Ritu, “Fuzzy Neuro Systems for Machine Learning for Large Data Sets”, Proceedings of the IEEE International Advance Computing Conference, ieeexplore, pp 541-545, Digital Object Identifier 10.1109/IADCC.2009.4809069, 6-7 March 2009, Patiala, India

  2. Data Size • In General, More the training data, better the performance • Large training sets • High dimensionality • High classification classes

  3. Problems in Neural Networks

  4. The Basic Idea

  5. The Algorithm

  6. Neural Network n Neural Network 2 Neural Network 3 Neural Network 1 The Hierarchical Nature Neural Network ………

  7. The Approach in Input Space 1 1 2 2 3 3 4 4 1 1 2 2 1 1

  8. Results

  9. Fuzzy C Means Clustering

  10. Results

  11. Conclusion • Training Time • Training Efficiency

  12. References • Alves, R.Lde.S.; de Melo, J.D.; Neto, A.D.D. and Albuquerque, A.C.M.L, “New parallel algorithms for back-propagation learning”, Proceedings of the 2002 International Joint Conference on Neural Networks, 2002. IJCNN '02, pp 2686-2691, 2002 • Amin, Md. Faijul; Murase, K., Single-layered complex-valued neural network for real-valued classification problems, Neurocomputing (2008), doi:10.1016/j.neucom.2008.04.006 • Ang, J.H. et al., Training neural networks for classification using growth probability-based evolution, Neurocomputing (2008), doi:10.1016/j.neucom.2007.10.011 • Azari, N.G. and Lee, S.-Y, Hybrid partitioning for particle-in-cell simulation on shared memory systems, Proc. of 11th International Conference on Distributed Computing Systems, 20-24 May 1991, pp. 526-533. • Babii, Sorin; Cretu, Vladimir; Petriu, Emil M., “Performance Evaluation of Two Distributed BackPropagation Implementations”, Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17, 2007 • Campobello, Giuseppe; Patane, Giuseppe and Russo, Marco, “An efficient algorithm for parallel distributed unsupervised learning”, Neurocomputing, Volume 71, Issues 13-15, August 2008, Pages 2914-2928 • Draghici Sorin, “A neural network based artificial vision system for licence plate recognition‟, international Journal of Network Security, International Journal of Neural Systems, Vol. 8, No. 1, 1997 • Er, Meng Joo; Zhou, Yi, ‘A novel framework for automatic generation of fuzzy neural networks’, Neurocomputing, 71 (2008) 584–591 • [9] Estevez, Pablo A; Paugam-Moisy, Helene, Puzenat , Didier , Ugarte, Manuel, “A scalable parallel algorithm for training a hierarchical mixture of neural experts”, Parallel Computing 28 (2002) 861–891 • Feng, Zhonghui; Zhou, Bing and Shen, Junyi, “A parallel hierarchical clustering algorithm for PCs cluster system”, Neurocomputing Volume 70, Issues 4-6, January 2007, Pages 809-818 • Graves Alex, Fernandez Santiago, Liwicki Marcus, Bunke Horst, Schmidhuber Jurgen, „Unconstrained Online Handwriting Recognition with Recurrent Neural Networks‟, Advances in Neural Information Processing Systems 20, 2008 • Hanzalek Zdenek, “A Parallel algorithm for gradient training of feedforward neural networks”, Pattern Computing, 24(1998), 823-839

  13. Jia, Zhen, Balasuriya, Arjuna and Challa, Subhash, ” Sensor fusion-based visual target tracking for autonomous vehicles with the out-of-sequence measurements solution”, Robotics and Autonomous Systems Volume 56, Issue 2, 29 February 2008, Pages 157-176 • Kak, S.C.;” New algorithms for training feedforward neural networks”, Pattern Recognition Letters, 15, 295-298 (1994). • Kak, Subhash C.,” On generalization by neural networks”, ELSEVIER Information Sciences 111 (1998) 293-302 • Lin, Cheng-Jian; Hong, Shang-Jin, ‘The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition’, Neurocomputing 71 (2007) 297–310 • Madineni, K.B., “ Two corner classi¯cation algorithms for training the Kakfeedforward neural network”. Information Sciences 81, 229-234 (1994). • Mohanty, S.; Bhattacharya, S; ‘Recognition of Voice signals for Oriya Language using wavelet Neural Network’, ACM International Journal of Expert Systems with Applications, Vol 34, Issue 3, pp 2130-2147, April 2008 • Ozdzynski, Piotr; Lin, Andy; Liljeholm, Mimi and Beatty, Jackson, “A parallel general implementation of Kohonen's self-organizing map algorithm: performance and scalability”, Neurocomputing Volumes 44-46, June 2002, Pages 567-571 • Pagac, D., Nebot, E. M. and Durrant. W., H., “An evidential approach to map building for autonomous robots,” IEEE Trans. On Robotics and Automation, vol.14, no.2, pp. 623-629, Aug. 1998. • Papakostas, G. A., Karras D. A., Mertzios B. G., and Boutalis, Y. S, ‘An Efficient Feature Extraction Methodology for Computer Vision Applications using Wavelet Compressed Zernike Moments’, ACM International Journal of Information Sciences, Vol 177, Issue 13, 2007 • Purwin, Oliver; D’Andrea, Raffaello; Lee, Jin-Woo; “Theory and implementation of path planning by negotiation for decentralized agents ”, Robotics and Autonomous Systems Volume 56, Issue 5, 31 May 2008, Pages 422-436 • Sandhu , Parvinder Singh; Salaria, Dalwinder Singh; Singh , Hardeep, ‘A Comparative Analysis of Fuzzy, Neuro-Fuzzy and Fuzzy-GA Based Approaches for Software Reusability Evaluation’, Proceedings of Worls Academy of Science, Engineering and Technology Volume 29 May, 2008, ISSN 1307-6884 • Shukla, Anupam; Tiwari, Ritu, ‘Fusion of Face and Speech Features with Artificial Neural Network for Speaker Authentication’, IETE Technical Review, Vol 24, No 5, September-October 2007, pp 359-368 • Suresh, S.; Omkar, S.N. and Mani, V, “Parallel Implementation of Back-Propagation Algorithm in Networks of Workstations”, IEEE Transactions on Parallel and Distributed Systems, Vol 16, No 1, pp 24-34, January, 2005 • Taur, J.S.; Tao, C.W., ‘A New Neuro-Fuzzy Classifier with Application to On-Line Face Detection and Recognition’, Journal of VLSI Signal Processing 26, 397–409, 2000 • Vieira, Armando; Barradas, Nuno; ‘A training algorithm for classification of high-dimensional data’, Neurocomputing 50 (2003) 461 – 472

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