Curriculum Vitae
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Contextual Self-Organizing Maps for Visual Design Space Exploration Brett Ekn Olny Iowa State University
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2010 Contextual self-organizing maps for visual design space exploration Brett ekN olny Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Mechanical Engineering Commons Recommended Citation Nekolny, Brett, "Contextual self-organizing maps for visual design space exploration" (2010). Graduate Theses and Dissertations. 11788. https://lib.dr.iastate.edu/etd/11788 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Contextual self-organizing maps for visual design space exploration by Brett Matthew Nekolny A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Co-majors: Human Computer Interaction; Mechanical Engineering Program of Study Committee: Eliot Winer, Major Professor Amy Kaleita Song Zhang Iowa State University Ames, Iowa 2010 Copyright © Brett Matthew Nekolny, 2010. All rights reserved ii TABLE OF CONTENTS 1 INTRODUCTION 1 1.1 PURPOSE 1 1.2 DESIGN SPACE 1 1.3 OPTIMIZATION 4 1.4 DESIGN AND OPTIMIZATION AND VISUALIZATION 6 1.5 MOTIVATION 7 1.6 THESIS ORGANIZATION 8 2 BACKGROUND 9 2.1 OPTIMIZATION VISUALIZATION -
Outline of Machine Learning
Outline of machine learning The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of computer science[1] (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. Contents What type of thing is machine learning? Branches of machine learning Subfields of machine learning Cross-disciplinary fields involving machine learning Applications of machine learning Machine learning hardware Machine learning tools Machine learning frameworks Machine learning libraries Machine learning algorithms Machine learning methods Dimensionality reduction Ensemble learning Meta learning Reinforcement learning Supervised learning Unsupervised learning Semi-supervised learning Deep learning Other machine learning methods and problems Machine learning research History of machine learning Machine learning projects Machine learning organizations Machine learning conferences and workshops Machine learning publications -
Application of Neural Networks to Data Mining
SQU Journal For Science, 12 (2) (2007)121-141 © 2007 Sultan Qaboos University Application of Neural Networks to Data Mining Zlatinka Svetoslavova Kovacheva Higher College of Technology, Al Khuwair, Muscat, Oman,E-mail: [email protected] ﺗطﺑﯾﻘﺎت اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﻓﻲ اﻟﺗﻧﻘﯾب ﻋن اﻟﺑﯾﺎﻧﺎت ذﻻﺗﯾﻧﻛﺎ ﺳﻔﯾﺗوﺳﻼﻓوﻓﺎ ﻛوﻓﺎﺗﺷﯾﻔﺎ ﺧﻼﺻﺔ : اﻟﺗﻧﻘﯾب ﻓﻲ اﻟﺑﯾﺎﻧﺎت ھو ﻋﻠم ﻣﮭم ﻻﺳﺗﺧﻼص اﻟﻣﻌﻠوﻣﺎت ﻣن اﻟﺑﯾﺎﻧﺎت واﻷرﻗﺎم اﻟﺗﻲ ﻓﻲ اﻷﻏﻠب ﻻ ﺗﻛون ﻣﻔﮭوﻣﺔ ﻟﻣﻌظم اﻟﻧﺎس ﺑﺣﯾث ﯾﺗم اﺳﺗﺧﻼص اﻟﻣﻌﻠوﻣﺎت وﺗﻘدﯾﻣﮭﺎ ﺑﻠﻐﺔ ﺳﮭﻠﺔ وﻣﻔﮭوﻣﺔ,ﻛﻣﺎ ﯾﻘوم ﺗﻧﻘﯾب اﻟﺑﯾﺎﻧﺎت ﺑﺎﺳﺗﺧدام اﻟﺑﯾﺎﻧﺎت اﻟﺣﺎﻟﯾﺔ اﻟﻣﺗوﻓرة ﻹﺳﺗﻧﺑﺎط ﺑﯾﺎﻧﺎت ﺟدﯾدة ﺗﺳﺗﺧدم ﻟﻌﻣل اﻟﺗوﻗﻌﺎت ﺣول ﺣﻘل ﻣﻌﯾن. اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ أﯾﺿﺎ ﺗﺳﺗﺧدم ﻛﺄداة ﺗﺣﻠﯾل ﺗﻛون ﻣﺻﻣﻣﺔ ﻟﺑﻧﺎء اﻟﻧﻣﺎذج اﻟﺗﻔﺳﯾرﯾﺔ وﻓﻲ اﻟوﻗت اﻟﺣﺎﻟﻲ ﺗوﺟد ﺷﺑﻛﺎت ﻋﺻﺑﯾﺔ ﺗﻘوم ﺑﺎﺳﺗﺧدام ﻋﻣﻠﯾﺎت ﺣﺳﺎﺑﯾﺔ ﻣﻌﻘدة ﺑﺷﻛل ﻣﺑﺎﺷر ﻟﺗﺳﺎھم ﻓﻲ ﻋﻣﻠﯾﺔ ﺑﻧﺎء اﻟﻧﻣﺎذج اﻟﺗﻔﺳﯾرﯾﺔ ﺣﯾث أن اﺧر ﺗطورات اﻟﺑﺣث ﻓﻲ اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﺗﺟﻌﻠﮭﺎ ﻗرﯾﺑﺔ ﺟدا ﻣن ﺗﻌرﯾف اﻟﺗﻧﻘﯾب ﻓﻲ اﻟﺑﯾﺎﻧﺎت ﻓﮭﻲ أﯾﺿﺎ ﺗﻘوم ﺑﺎﺳﺗﺧﻼص اﻟﻣﻌﻠوﻣﺎت ﻣن اﻟﺑﯾﺎﻧﺎت اﻟﻐﺎﻣﺿﺔ ﻟﺗﻘدﻣﮭﺎ ﺑﻣﺻطﺣﺎت واﺿﺣﺔ وﻣﻔﮭوﻣﺔ ﻟﻠﺟﻣﯾﻊ. اﻟﮭدف اﻷﺳﺎﺳﻲ ﻣن ھذه اﻟﻣراﺟﻌﺔ ھو ﻣﻘﺎرﻧﺔ اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﺑﻐﯾرھﺎ ﻣن أﺳﺎﻟﯾب ﺗﻧﻘﯾب اﻟﺑﯾﺎﻧﺎت وﻟﻌرض ﺑﻌض اﻷﻣﺛﻠﺔ واﻟﺗطﺑﯾﻘﺎت ﻟدوراﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﻓﻲ ﺗﻧﻘﯾب اﻟﺑﯾﺎﻧﺎت ﻋﻠﻰ اﻟواﻗﻊ. ABSTRACT: Data Mining accomplishes nontrivial extraction of implicit, previously unknown, and potentially useful information of data. The aim of data mining is to discover knowledge out of data and present it in a form that is easily comprehensible to humans. Neural Networks are analytic techniques capable of predicting new observations from other observations after executing a process of so-called learning from existing data. Neural Network techniques can also be used as a component of analyses designed to build explanatory models. Now there is neural network software that uses sophisticated algorithms directly contributing to the model building process. -
Predictive Analytics Using R
Predictive Analytics using R Dr. Jeffrey Strickland is a Senior Predictive Analytics Consultant with over 20 years of Predictive Analytics expereince in multiple industiries including financial, insurance, defense and NASA. He is a subject matter expert on mathematical and statistical modeling, as well as machine using R learning. He has published numerous books on modeling and simulation. Dr. Strickland resides in Colorado. This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling, both statistical (parametric and nonparametric), as well as machine learning. We define predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics (statistical inference, models, tests, etc.) — this is an advanced book. Some theoretical foundations are laid out (perhaps subtlety) but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror at http://www.r-project.org/. An introduction to R is also available at http://cran.r-project.org /doc/manuals/r-release/R-intro.html. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series.