Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Supported and liquid phase task specific ionic liquids for base catalysed Knoevenagel reactions

A series of Hunig's base tethered ammonium ionic liquids have been used to catalyse the Knoevenagel condensation of aldehydes/ketones with malononitrile and ethyl cyanoacetate. The reactions were performed under homogeneous and under biphasic, liquid-liquid and liquid-silica supported ionic liquid, conditions with the biphasic systems employing cyclohexene as the second phase. By increasing the distance between the ammonium head group and Hunig's base the activity of the catalyst was found to increase. Higher activity, in general, was found under homogeneous reaction conditions; however, the recyclability of the catalyst was improved by supporting the BIL under biphasic conditions. (c) 2007 Elsevier B.V. All rights reserved.

The role of the primary motor cortex during skill acquisition on a two-degrees-of-freedom movement task

One can partially eliminate motor skills acquired through practice in the hours immediately following practice by applying repetitive transcranial stimulation (rTMS) over the primary motor cortex. The disruption of acquired levels of performance has been demonstrated on tasks that are ballistic in nature. The authors investigated whether motor recall on a discrete aiming task is degraded following a disruption of the primary motor cortex induced via rTMS. Participants (N = 16) maintained acquired performance levels and patterns of muscle activity following the application of rTMS. despite a reduction in corticospinal excitability. Disruption of the primary motor cortex during a consolidation period did not influence the retention of acquired skill in this type of discrete visuomotor task.