Fuzzy Neuro Systems for Machine Learning for Large Data Sets - PowerPoint PPT Presentation

1 / 13
About This Presentation
Title:

Fuzzy Neuro Systems for Machine Learning for Large Data Sets

Description:

Fuzzy Neuro Systems for Machine Learning for Large Data Sets – PowerPoint PPT presentation

Number of Views:164
Avg rating:3.0/5.0
Slides: 14
Provided by: rahulkalaa
Category:

less

Transcript and Presenter's Notes

Title: Fuzzy Neuro Systems for Machine Learning for Large Data Sets


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_at_yahoo.co.in, rkala_at_students.iiitm.a
    c.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
The Hierarchical Nature

7
The Approach in Input Space
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),
    doi10.1016/j.neucom.2008.04.006
  • Ang, J.H. et al., Training neural networks for
    classification using growth probability-based
    evolution, Neurocomputing (2008),
    doi10.1016/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) 584591
  • 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) 861891
  • 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) 297310
  • Madineni, K.B., Two corner classication
    algorithms for training the Kak feedforward
    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 DAndrea, 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, 397409, 2000
  • Vieira, Armando Barradas, Nuno A training
    algorithm for classification of high-dimensional
    data, Neurocomputing 50 (2003) 461 472
Write a Comment
User Comments (0)
About PowerShow.com