Distributed machine learning in networks by consensus

Neurocomputing

Volumn 124, Issue , 2014, Pages 2-12

  Georgopoulos, Leonidas ^a   Hasler, Martin ^b  

^a not available   (Switzerland)

^b EPFL   (Switzerland)

Author keywords

Consensus; Distributed machine learning; Gradient descent; Neural networks; Parallel machine learning; Peer to peer learning

Indexed keywords

BACKPROPAGATION; CLASSIFICATION (OF INFORMATION); FEEDFORWARD NEURAL NETWORKS; GRADIENT METHODS; NEURAL NETWORKS; PEER TO PEER NETWORKS;

CONSENSUS; DISTRIBUTED MACHINE LEARNING; GRADIENT DESCENT; PARALLEL MACHINE; PEER TO PEER;

LEARNING SYSTEMS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER NETWORK; CONCEPTUAL FRAMEWORK; CONSENSUS; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; THEORETICAL MODEL;

EID: 84885840172     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.12.055     Document Type: Article

References (16)

1. J.N. Tsitsiklis, Problems in Decentralized Decision Making and Computation, Ph.D. Thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1984.
2. Xiao L., Boyd S., Kim S.-J. Distributed average consensus with least-mean-square deviation. J. Parallel Distrib. Comput. 2007, 67:33-46.
3. R. Olfati-saber, J.A. Fax, R.M. Murray, Consensus and cooperation in networked multi-agent systems, in: Proceedings of the IEEE, 2007.
4. L. Georgopoulos, Definitive Consensus for Distributed Data Inference, Ph.D. Thesis, École Polytechnique FéDérale de Lausanne, 2011.
5. M. Rabbat, R. Nowak, Distributed optimization in sensor networks, in: In Third International Symposium on Information Processing in Sensor Networks (IPSN 04), 2004, pp. 20-27.
6. Vapnik V. An overview of statistical learning theory. IEEE Trans. Neural Networks 1999, 10:988-999.
7. Navia-Vazquez A., Gutierrez-Gonzalez D., Parrado-Hernandez E., Navarro-Abellan J. Distributed support vector machines. IEEE Trans. Neural Networks 2006, 17:1091-1097.
8. Lu Y., Roychowdhury V., Vandenberghe L. Distributed parallel support vector machines in strongly connected networks. IEEE Trans. Neural Networks 2008, 19:1167-1178.
9. Ang H., Gopalkrishnan V., Hoi S., Ng W. Cascade RSVMin peer-to-peer networks. Mach. Learn. Knowl. Discovery Databases 2008, 55-70.
10. W. Kowalczyk, N. Vlassis, Newscast em, in: In NIPS 17, MIT Press, 2005, pp. 713-720.
11. Kokiopoulou E., Frossard P. Graph-based classification of multiple observation sets. Pattern Recognition 2010, 43:3988-3997.
12. F. Benezit, P. Thiran, M. Vetterli, Interval consensus: from quantized gossip to voting, in: IEEE, ICASSP 2009, pp. 3661-3664.
13. F. Benezit, Distributed Average Consensus for Wireless Sensor Networks, Ph.D. Thesis, Information and Communications Sciences, Lausanne, 2009.
14. Bishop C.M. Neural Networks for Pattern Recognition 1996, Oxford University Press, USA. 1 ed.
15. D. Zhou, O. Bousquet, T.N. Lal, J. Weston, B. Schlkopf, Learning with local and global consistency, in: Advances in Neural Information Processing Systems, vol. 16, MIT Press, 2004, pp. 321-328.
16. G. Cormack, T. Lynam, Spam corpus creation for TREC, in: Stanford University.