Nonsmooth optimization algorithm for semi-supervised data classification

In this paper, we develop a new algorithm for solving semi-supervised data classification problems. Given a training set of labeled data and a working set of unlabeled data, semi-supervised vector machine constructs a support vector machine using both the training and working sets. We formulate this problem as a nonsmooth optimization problem and then apply the quasisecant method for its solution. We evaluate the new algorithm applying it to some test data sets and report the results of numerical experiments. Copyright © 2010 Watam Press

Linear separability: Quasisecant method and application to semi-supervised data classification

24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010 -- 23 June 2010 through 26 June 2010 -- Izmir -- 106702In this paper we have proposed a semi-supervised algorithm based on quasisecant optimization method for solving data classification problems. The algorithm computes hyperplane(s) to separate two sets with respect to some tolerance. An error function is formulated and an algorithm for its minimization is expressed. We present results of numerical experiments using several UCI test data sets and compare the proposed algorithm with two supervised data classification algorithm (linear separability, max-min separability) and two support vector machine solvers (LIBSVM and SVM-light). © Izmir University of Economics, Turkey, 2010

AN INCREMENTAL NONSMOOTH OPTIMIZATION ALGORITHM FOR CLUSTERING USING L1 AND L? NORMS

2-s2.0-85096898399An algorithm is developed for solving clustering problems with the similarity measure defined using the L1and L? norms. It is based on an incremental approach and applies nonsmooth optimization methods to find cluster centers. Computational results on 12 data sets are reported and the proposed algorithm is compared with the X-means algorithm. © 2020, JOURNAL OF INDUSTRIAL AND JOURNAL OF INDUSTRIAL ANDChongqing Normal University, CNU Australian Government Australian Research Council, ARC: DP190100580The authors would like to thank two anonymous referees for their comments that helped to improve the quality of the paper. This research by Dr. A. Bagirov was funded by the Australian Government through the Australian Research Council?s Discovery Projects scheme (Project No.: DP190100580). The paper was completed when Dr. A. Bagirov visited Chongqing Normal University, China.Acknowledgements. The authors would like to thank two anonymous referees for their comments that helped to improve the quality of the paper. This research by Dr. A. Bagirov was funded by the Australian Government through the Australian Research Council’s Discovery Projects scheme (Project No.: DP190100580). The paper was completed when Dr. A. Bagirov visited Chongqing Normal University, China

Attitudes of the Third-Year Nursing Students Toward Organ Donation: Cross-Sectional Study

Background. Health professionals can affect attitudes toward organ donation in society; therefore, priority should be given to exploration of attitudes of nursing students as important prospective members of the health profession. The goal of this study was to assess nursing students' attitudes and volunteerism toward organ donation

NACA Research Memorandums

Report presenting an investigation of a liquid-hydrogen fuel system developed to operate one of the turbojet engines in a twin-engine light bomber at an altitude of 50,000 feet and Mach number 0.75. The heat exchanger was evaluated in an altitude test chamber in conjunction with the complete aircraft fuel system. Results regarding calculated heat-exchanger performance, experimental heat-exchanger performance, and reliability are provided

An incremental clustering algorithm based on hyperbolic smoothing

WOS: 000351906500010Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex optimization problem. For the most global optimization techniques this problem is challenging even in medium size data sets. In this paper, we propose an approach that allows one to apply local methods of smooth optimization to solve the clustering problems. We apply an incremental approach to generate starting points for cluster centers which enables us to deal with nonconvexity of the problem. The hyperbolic smoothing technique is applied to handle nonsmoothness of the clustering problems and to make it possible application of smooth optimization algorithms to solve them. Results of numerical experiments with eleven real-world data sets and the comparison with state-of-the-art incremental clustering algorithms demonstrate that the smooth optimization algorithms in combination with the incremental approach are powerful alternative to existing clustering algorithms.Australian Research CouncilAustralian Research Council [DP140103213]Dr. Burak Ordin acknowledges TUBITAK for its support of his visit to the University of Ballarat, Australia. This research by A. M. Bagirov was supported under Australian Research Council's Discovery Projects funding scheme (Project No. DP140103213). We are grateful to two anonymous referees for their comments and criticism that helped the authors to significantly improve the quality of the paper