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R. Salas, C. Saavedra , H. Allende, C. Moraga PRL, 2011 Presented by Hung-Yi Cai 2011/5/25

Machine fusion to enhance the topology preservation of vector quantization artificial neural networks. R. Salas, C. Saavedra , H. Allende, C. Moraga PRL, 2011 Presented by Hung-Yi Cai 2011/5/25. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments.

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R. Salas, C. Saavedra , H. Allende, C. Moraga PRL, 2011 Presented by Hung-Yi Cai 2011/5/25

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  1. Machine fusion to enhance the topology preservation of vector quantization artificial neural networks R. Salas, C. Saavedra, H. Allende, C. Moraga PRL, 2011 Presented by Hung-Yi Cai 2011/5/25

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • The objective of VQ is to preserve the topological relationships existing in a data set and to project the data to a lattice of lower dimensions. • It’s difficult to properly specify the structure of the lattice that best preserves the topology of the data.

  4. Objectives • Bagging • Boosting To propose a merging algorithms for machine-fusion, boosting-fusion-based and hybrid-fusion ensembles of SOM, NG and GSOM networks.

  5. Methodology • Machine fusion method for the ensemble of VQ-ANN

  6. Methodology • Machine fusion method for the ensemble of VQ-ANN

  7. Methodology • Boosting machine fusion method

  8. Experiments Synthetic Data

  9. Experiments Real Data

  10. Conclusions • The main goal of this paper is to improve the topology preservationby combining the output of several VQ-ANN. • The proposed ensemble schemes were able to improve the quality of topological representation compared to their respective base single networks.

  11. Comments • Advantages • Improving the VQ in the ANN. • Drawbacks • The methods don’t consider the outliers. • Applications • Vector Quantization

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