This thesis research offers two contributions: (1) a new user-friendly graphical user interface that can be used in a feature screening process, (2) a new algorithm for feature screening in a situation where there is a high ratio of feature to exemplars. The first objective is achieved by creating a MATLAB based graphical user interface, which is named as STNGER. STNGER is evaluated on both abstract and the real life problems and provides promising results. For the second objective a new algorithm is suggested. This new algorithm is based on the SNR screening method. By means of this new algorithm, the SNR screening method can determine the salient features in a situation where there is a high ratio of features to exemplars. The performance of the new algorithm suggests that one can apply the SNR screening method to randomly chosen subsets of the data and retain the best features from each subset for subsequent analysis. In this fashion, noisy features are removed while creating new subsets with salient features. The new algorithm is demonstrated on both real-life and the well-defined abstract problems.
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Taschenbuch. Etat : Neu. Neuware - This thesis research offers two contributions: (1) a new user-friendly graphical user interface that can be used in a feature screening process, (2) a new algorithm for feature screening in a situation where there is a high ratio of feature to exemplars. The first objective is achieved by creating a MATLAB based graphical user interface, which is named as STNGER. STNGER is evaluated on both abstract and the real life problems and provides promising results. For the second objective a new algorithm is suggested. This new algorithm is based on the SNR screening method. By means of this new algorithm, the SNR screening method can determine the salient features in a situation where there is a high ratio of features to exemplars. The performance of the new algorithm suggests that one can apply the SNR screening method to randomly chosen subsets of the data and retain the best features from each subset for subsequent analysis. In this fashion, noisy features are removed while creating new subsets with salient features. The new algorithm is demonstrated on both real-life and the well-defined abstract problems.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. N° de réf. du vendeur 9781288305858
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Etat : New. KlappentextrnrnThis thesis research offers two contributions: (1) a new user-friendly graphical user interface that can be used in a feature screening process, (2) a new algorithm for feature screening in a situation where there is a high ratio . N° de réf. du vendeur 6554398
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