The research contribution of this thesis is the first known integrated architecture and feature selection algorithm for Radial Basis Neural Networks (RBNN's). The objective is to apply the network iteratively to determine the final architecture and feature set used to evaluate a problem. Additionally, this thesis compares three different classification techniques, Discriminant Analysis (DA), Feed-Forward Neural Networks (FFN) and RBNN's against several hard to solve problems. These problems were used to evaluate general classifier performance as well as the performance of the feature selection techniques. This thesis describes the classification techniques as well as the measures used to evaluate them. It next develops a new clustering technique used to determine the network architecture and the saliency measure used to select features for RBNN's. Next, the thesis applies these techniques to three general problems, Block-C, the University of Wisconsin Breast Cancer Data (UWBCD) and a noise corrupted version of Fisher's Iris problem. Finally, the conclusions and recommendations for future research are provided.
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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New. Bestandsnummer des Verkäufers 19399217-n
Anzahl: Mehr als 20 verfügbar
Anbieter: BargainBookStores, Grand Rapids, MI, USA
Paperback or Softback. Zustand: New. An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks. Book. Bestandsnummer des Verkäufers BBS-9781288282388
Anbieter: PBShop.store US, Wood Dale, IL, USA
PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9781288282388
Anzahl: Mehr als 20 verfügbar
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9781288282388
Anzahl: Mehr als 20 verfügbar
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Zustand: New. Print on Demand pp. 86. Bestandsnummer des Verkäufers 390044103
Anzahl: 4 verfügbar
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In. Bestandsnummer des Verkäufers ria9781288282388_new
Anzahl: Mehr als 20 verfügbar
Anbieter: Books Puddle, New York, NY, USA
Zustand: New. Print on Demand pp. 86. Bestandsnummer des Verkäufers 26390604312
Anzahl: 4 verfügbar
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. PRINT ON DEMAND pp. 86. Bestandsnummer des Verkäufers 18390604306
Anzahl: 4 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: New. Bestandsnummer des Verkäufers 19399217-n
Anzahl: Mehr als 20 verfügbar
Anbieter: moluna, Greven, Deutschland
Zustand: New. KlappentextrnrnThe research contribution of this thesis is the first known integrated architecture and feature selection algorithm for Radial Basis Neural Networks (RBNN s). The objective is to apply the network iteratively to determine the fina. Bestandsnummer des Verkäufers 6552916
Anzahl: Mehr als 20 verfügbar