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Computational Intelligence Magazine, IEEE

Issue 2 • Date May 2009

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Displaying Results 1 - 17 of 17
  • IEEE Computational Intelligence Magazine

    Publication Year: 2009 , Page(s): C1
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  • Table of contents

    Publication Year: 2009 , Page(s): 1
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  • Learning and Intelligence [Editor's remarks]

    Publication Year: 2009 , Page(s): 2
    Cited by:  Papers (2)
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  • My Mentor Wirt [President's message]

    Publication Year: 2009 , Page(s): 3 - 4
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  • Career Profile: Interview with Evangelia Micheli-Tzanakou, Rutgers University, USA

    Publication Year: 2009 , Page(s): 5 - 7
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    Evangelia Micheli-Tzanakou discusses her research and career. View full abstract»

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  • Introduction to New IEEE CIS Educational Activities [Society briefs]

    Publication Year: 2009 , Page(s): 8 - 9
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  • IEEE Fellows - Class of 2009 [Society news]

    Publication Year: 2009 , Page(s): 9 - 15
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  • Newly elected CIS Administrative Committee Members (2009-2011)

    Publication Year: 2009 , Page(s): 15 - 17
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  • France Chapter Report

    Publication Year: 2009 , Page(s): 18 - 21
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  • Conference report: 2008 IEEE Swarm Intelligence Symposium (SIS 2008)

    Publication Year: 2009 , Page(s): 20 - 21
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  • IEEE Transactions on Autonomous Mental Development - Call for Papers

    Publication Year: 2009 , Page(s): 22
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  • Time Series Prediction Using Support Vector Machines: A Survey

    Publication Year: 2009 , Page(s): 24 - 38
    Cited by:  Papers (41)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7753 KB) |  | HTML iconHTML  

    Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting , weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of the time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms. This paper provides a survey of time series prediction applications using a novel machine learning approach: support vector machines (SVM). The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons.The ultimate goal is to provide the reader with insight into the applications using SVM for time series prediction, to give a brief tutorial on SVMs for time series prediction, to outline some of the advantages and challenges in using SVMs for time series prediction, and to provide a source for the reader to locate books, technical journals, and other online SVM research resources. View full abstract»

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  • Adaptive Dynamic Programming: An Introduction

    Publication Year: 2009 , Page(s): 39 - 47
    Cited by:  Papers (92)
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    In this article, we introduce some recent research trends within the field of adaptive/approximate dynamic programming (ADP), including the variations on the structure of ADP schemes, the development of ADP algorithms and applications of ADP schemes. For ADP algorithms, the point of focus is that iterative algorithms of ADP can be sorted into two classes: one class is the iterative algorithm with initial stable policy; the other is the one without the requirement of initial stable policy. It is generally believed that the latter one has less computation at the cost of missing the guarantee of system stability during iteration process. In addition, many recent papers have provided convergence analysis associated with the algorithms developed. Furthermore, we point out some topics for future studies. View full abstract»

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  • Comments on the Paper by Perlovsky, Entitled "Integrating Language and Cognition"

    Publication Year: 2009 , Page(s): 48 - 49
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  • A perspective on computational intelligence education

    Publication Year: 2009 , Page(s): 50 - 51
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    Presents an interview with Joe Rothermich, a Vice President at Lincoln Vale, LLC, an alternative asset management firm. View full abstract»

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  • Applications of Fuzzy Logic in Bioinformatics (Dong Xu et al.; 2008) [Book review]

    Publication Year: 2009 , Page(s): 52 - 54
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  • Conference calendar

    Publication Year: 2009 , Page(s): 55
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope