Improvements to the SMO algorithm for SVM regression

10.1109/72.870050IEEE Transactions on Neural Networks1151188-1193ITNN

Support Vector Machines (SVMs) for Monitoring Network Design

In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set. Due to Mercer\u27s conditions on the kernels, the corresponding optimization problems are convex and hence have no local minima. In this paper, SVMs are illustratively used to reproduce the behavior of Monte Carlo–based flow and transport models that are in turn used in the design of a ground water contamination detection monitoring system. The traditional approach, which is based on solving transient transport equations for each new configuration of a conductivity field, is too time consuming in practical applications. Thus, there is a need to capture the behavior of the transport phenomenon in random media in a relatively simple manner. The objective of the exercise is to maximize the probability of detecting contaminants that exceed some regulatory standard before they reach a compliance boundary, while minimizing cost (i.e., number of monitoring wells). Application of the method at a generic site showed a rather promising performance, which leads us to believe that SVMs could be successfully employed in other areas of hydrology. The SVM was trained using 510 monitoring configuration samples generated from 200 Monte Carlo flow and transport realizations. The best configurations of well networks selected by the SVM were identical with the ones obtained from the physical model, but the reliabilities provided by the respective networks differ slightly

The lure of work-life benefits : perceived person-organization fit as a mechanism explaining job seeker attraction to organizations

Drawing from Person-Organization (P-O) fit theory, we explain how the provision of work-life benefits (WLBs) increases job seeker attraction to organizations during the early recruitment stage because of a perceived value fit between job seekers and the organization. Our results from an experimental study using a sample of 189 MBA students who belonged to two generational groups (Millennials and Gen X-ers) and were seeking employment during a period of economic recession support our expectations. We found that job seekers develop higher P-O fit perceptions for organizations that supplement standard pay with WLBs in their recruitment materials in comparison to organizations that supplement standard pay with healthcare benefits or offer only standard pay. In turn, such organizations are assessed as more attractive prospective employers. We also found that generational group moderated the path between P-O fit and job seeker attraction such that Millennial job seekers were more likely to be attracted towards organizations with which they had strong fit perceptions than their Gen X counterparts. Theoretical and practical implications of our findings are discusse