ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING

Authors

  • Ankush Rai School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India
  • Jagadeesh Kannan R School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19738

Keywords:

On-line learning, Image processing over network

Abstract

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency.  In this study we present a novel online learning algorithm for network based computational operations in image processing setting

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References

Bennett KP, Mangasarian OL. Robust linear programming discrimination of two linearly inseparable sets. Optim Methods Softw 1992;1(1):23-34.

Cauwenberghs G, Poggio T. Incremental and decremental support vector machine learning. In: Leen TK, Dietterich TG, Tresp V, editors. Advances in Neural Information Processing Systems. Vol. 13. Cambridge, MA: MIT Press; 2001. p. 409-15.

Csat´o L, Opper M. Sparse representation for Gaussian process models. In: Leen TK, Dietterich TG, Tresp V, editors. Advances in Neural Information Processing Systems. Vol. 13. Cambridge, MA: MIT Press; 2001. p. 444-50.

Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression: A Statistical View of Boosting. Technical Report, Stanford University, Department of Statistics; 1998.

Gentile C. A new approximate maximal margin classification algorithm. In: Leen TK, Dietterich TG, Tresp V, editors. Advances in Neural Information Processing Systems. Vol. 13. Cambridge, MA: MIT Press; 2001. p. 500-6.

Graepel T, Herbrich R, Williamson RC. From margin to sparsity. In: Leen TK, Dietterich TG, Tresp V, editors. Advances in Neural Information Processing Systems. Vol. 13. Cambridge, MA: MIT Press; 2001. p. 210-6.

Guo Y, Bartlett P, Smola AJ, Williamson RC. Norm-based regularization of boosting. Submitted J Mach Learn Res 2001:1001-22.

Herbster M. Learning Additive Models Online with Fast Evaluating Kernels. In: Proceedings. 14th Annual Conference on Computational Learning Theory (COLT). Springer; 2001. p. 444-60.

Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12(7):629-39.

Rai A, Ramanathan S. Distributed learning in networked controlled cyber physical system. Int J Pharm Technol 2016;8(3):18537-46.

Ankush R. Application of Artificial Intelligence for Virtually Assisted Prognosis of Diabetes: A Nodds Project. IJCA Proceedings on National Seminar on Application of Artificial Intelligence in Life Sciences. NSAAILS(1):1-5, February; 2013.

Rai A, Ramanathan S, Kannan RJ. Quasi Opportunistic Supercomputing for Geospatial Socially Networked Mobile Devices. Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2016 IEEE 25th International Conference on IEEE; 2016.

Rai A. Unsupervised Probabilistic Debugging. Recent Trends Program Lang 2015;1(2, 3):14-6.

Published

01-04-2017

How to Cite

Rai, A., and J. K. R. “ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING”. Asian Journal of Pharmaceutical and Clinical Research, vol. 10, no. 13, Apr. 2017, pp. 284-7, doi:10.22159/ajpcr.2017.v10s1.19738.

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