Reply to Melissa Moschella

Professor Moschella begins by discussing confusions in the brain death debate surrounding the use of the concepts of “integration” and “wholeness.” Some scholars, she says, such as Alan Shewmon, take the presence of biological integration as an indication of ontological wholeness. Others, such as the members of the President’s Council for Bioethics, think that some bodily integration can persist in the body of a brain-dead individual; but that the subject in which it persists in not a whole

Let\u27s Go To The Movies

https://digitalcommons.library.umaine.edu/mmb-vp/1977/thumbnail.jp

Parallel Support Vector Machines

The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained widespread use in recent years because of successful applications like character recognition and the profound theoretical underpinnings concerning generalization performance. Yet, one of the remaining drawbacks of the SVM algorithm is its high computational demands during the training and testing phase. This article describes how to efficiently parallelize SVM training in order to cut down execution times. The parallelization technique employed is based on a decomposition approach, where the inner quadratic program (QP) is solved using Sequential Minimal Optimization (SMO). Thus all types of SVM formulations can be solved in parallel, including C-SVC and nu-SVC for classification as well as epsilon-SVR and nu-SVR for regression. Practical results show, that on most problems linear or even superlinear speedups can be attained