Open AccessDissertation
Predicting diffusion of innovations with self-organisation and machine learning
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Predicting diffusion of innovations with self-organisation and machine learning Master’s thesis 2003 75 pages, 24 figures, 6 tables and 2 appendices.Abstract:
Lappeenranta University of Technology Department of Information Technology Jarmo Ilonen Predicting diffusion of innovations with self-organisation and machine learning Master’s thesis 2003 75 pages, 24 figures, 6 tables and 2 appendices. Supervisors: Professor Heikki Kalviainen, Professor Seppo Pitkanenread more
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
The Self-Organizing Map
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
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Cross-National Diffusion Research: What Do We Know and How Certain Are We? - Rarely Published but Quite Contrary
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Toward automatic forecasts for diffusion of innovations
TL;DR: An automated framework for forecasting the diffusion of innovations, which utilizes existing diffusion information from any market areas or similar products introduced to the markets earlier, and which aims to move present theory toward more practical and automatic prediction tools for company analysts and diffusion researchers.
Book ChapterDOI
An Integrated Approach Using Data Mining and System Dynamics to Policy Design: Effects of Electric Vehicle Adoption on CO2 Emissions in Singapore
Bohao Zhang,Francis E. H. Tay +1 more
TL;DR: The current policies put in place to encourage electric vehicle (EV) adoption are insufficient for Singapore to electrify 50% of its vehicle population by the year 2050, and the projected CO2 emission is significantly lower than the year 2005 business as usual scenario.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Journal ArticleDOI
Deterministic nonperiodic flow
TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.