SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples - Kumar, R.; Jayaraman, Valadi K.; Kulkarni, B. D. (Pattern Recognition, 2005)
A hybrid technique involving symbolization of data to remove noise and use of conditional entropy minima to extract relevant and non-redundant features is proposed in conjunction with support vector machines to obtain more robust classification algorithm. The technique tested on three data sets shows improvements in classification efficiencies. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords: classification; conditional entropy; SVM; symbolization

SVR-based prediction of point gas hold-up for bubble column reactor through recurrence quantification analysis of LDA time-series - Gandhi, Ankit B.; Joshi, J. B.; Kulkarni, A. A.; Jayaraman, Valadi K.; Kulkarni, B. D. (International Journal of Multiphase Flow, 2008)
Recurrence quantification analysis (RQA) has emerged as a useful tool for detecting singularities in nonstationary time-series data. In this paper, we use RQA to analyze the velocity-time data acquired using laser doppler anemometry (LDA) signals in a bubble column reactor for Single point and Multipoint point spargers. The recurring dynamical states within the velocity-time-series occurring due to the bubble and the liquid passage at the point of measurement, are quantified by RQA features (namely % Recurrence, % Determinism, % Laminarity and Entropy), which in turn are regressed using support vector regression (SVR) to predict the point gas hold-up values. It has been shown that SVR-based model for the bubble column reactor can be potentially useful for online prediction and monitoring of the point gas hold-up for different sparging conditions. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords: Bubble column; Gas hold-up; LDA; Recurrence quantification analysis (RQA); Support vector regression (SVR)