36 resultados para Classical orthogonal polynomials


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Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

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The reaction mechanism of the Beckmann rearrangement over B2O3/gamma-Al2O3 and TS-1 in the gas phase has been investigated by isotope labeling approach. The isotopic labeled products were measured by mass spectrometry method. By exchanging oxygen with H, 180 in the rearrangement step, it was found that the exchange reaction between cyclohexanone oxime and (H2O)-O-18 over B2O3/-gamma-Al2O3 and TS-1 could only be carried out in some extent. It suggested that the dissociation of nitrilium, over solid acids be not completely free as the classical mechanism. A concept of the dissociation degree (alpha) that is defined as the ratio of the dissociated intermediate nitrilium to the total intermediate nitrilium has been proposed. By fitting the experimental values with the calculation equation of isotopic labeled products, it is obtained that a values for B2O3/-gamma-Al2O3 and TS-1 are 0.199 and 0.806 at the reaction conditions, respectively.

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In chemistry for chemical analysis of a multi-component sample or quantitative structure-activity/property relationship (QSAR/QSPR) studies, variable selection is a key step. In this study, comparisons between different methods were performed. These methods include three classical methods such as forward selection, backward elimination and stepwise regression; orthogonal descriptors; leaps-and-bounds regression and genetic algorithm. Thirty-five nitrobenzenes were taken as the data set. From these structures quantum chemical parameters, topological indices and indicator variable were extracted as the descriptors for the comparisons of variable selections. The interesting results have been obtained. (C) 2001 Elsevier Science B.V. All rights reserved.

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In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.

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The present work is first reporting the hemolytic activity of venom from jellyfish Rhopilema esculentum Kishinouye extracted by different phosphate buffer solutions and incubated at different temperature according to the orthogonal test L6(1) x 3(6). Of the seven controllable independent variables, incubated temperature and phenylmethylsulfonyl fluoride (PMSF) had strongest effect on the hemolytic activity. (c) 2006 Elsevier B.V. All rights reserved.