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  1. From Classical Connectionist Models to Probabilistic/Generalised Regression Neural Networks (PNNs/GRNNs)

    This chapter begins by briefly summarising some of the well-known classical connectionist/artificial neural network models such as multi-layered...
    Tetsuya Hoya in Artificial Mind System - Kernel Memory Approach
    Chapter

  2. Data Mining and User Profiling for an E-Commerce System

    Many companies are now develo** an online internet presence to sell or promote their products and services. The data generated by e-commerce sites...
    Ken McGarry, Andrew Martin, Dale Addison in Classification and Clustering for Knowledge Discovery
    Chapter

  3. Ontology-based Fuzzy Decision Agent and Its Application to Meeting Scheduling Support System

    A Fuzzy Decision Agent (FDA) based on personal ontology for Meeting Scheduling Support System (MSSS) is proposed in this chapter. In this system,...
    Chang-Shing Lee, Hei-Chia Wang, Meng-Ju Chang in Classification and Clustering for Knowledge Discovery
    Chapter

  4. D-GridMST: Clustering Large Distributed Spatial Databases

    In this paper, we will propose a novel distributable clustering algorithm, called Distributed-GridMST (D–GridMST for short), which deals with large...
    Ji Zhang, Han Liu in Classification and Clustering for Knowledge Discovery
    Chapter

  5. A Probabilistic Approach to Mining Fuzzy Frequent Patterns

    Deriving association rules is a typical task in data mining. The problem was originally defined for transactions of discrete items, but it was soon...
    Attila Gyenesei, Jukka Teuhola in Classification and Clustering for Knowledge Discovery
    Chapter

  6. Data Mining of Missing Persons Data

    This paper presents the results of analysis to evaluate the effectiveness of data mining techniques to predict the outcome for missing persons cases....
    K. Blackmore, T. Bossomaier, ... D. Thomson in Classification and Clustering for Knowledge Discovery
    Chapter

  7. 6 Beamforming Combined with Multi-channel Acoustic Echo Cancellation

    For audio signal acquisition, beamforming microphone arrays can be efficiently used for enhancing a desired signal while suppressing...
    Wolfgang Herbordt in Sound Capture for Human/Machine Interfaces
    Chapter

  8. 4 Optimum Beamforming for Wideband Non-stationary Signals

    Array processing techniques strive for extraction of maximum information from a propagating wave field using groups of sensors, which are located at...
    Wolfgang Herbordt in Sound Capture for Human/Machine Interfaces
    Chapter

  9. 9. Switched Scalable Video Coding Systems

    Scalable video coding means that a video sequence is only encoded once and the coded bitstream has the capability of sending any sub-bitstream...
    Zhengguo Li, Yengchai Soh, Changyun Wen in Switched and Impulsive Systems
    Chapter

  10. 4. Stability of Nonlinear Switched and Impulsive Systems

    In this chapter, we shall study the stability of nonlinear switched and impulsive systems of the form...
    Zhengguo Li, Yengchai Soh, Changyun Wen in Switched and Impulsive Systems
    Chapter

  11. Linkage Learning Genetic Algorithm

    In order to handle linkage evolution and to tackle the ordering problem, Harik [47] took Holland’s call [53] for the evolution of tight linkage quite...
    Ying-** Chen in Extending the Scalability of Linkage Learning Genetic Algorithms
    Chapter

  12. Preliminaries: Assumptions and the Test Problem

    After introducing the background and motivation of the linkage learning genetic algorithm, we will start to improve and understand the linkage...
    Ying-** Chen in Extending the Scalability of Linkage Learning Genetic Algorithms
    Chapter

  13. Modeling of Fuzzy Data

    Fuzzy data are imprecise data obtained from measurements, perception or by interviewing people. Typically, those data are expressed in linguistic...
    Hung T. Nguyen, Berlin Wu in Fundamentals of Statistics with Fuzzy Data
    Chapter

  14. Data Driven Fuzzy Modelling with Neural Networks

    Extraction of models for complex systems from numerical data of behavior is studied. In particular, systems representable as sets of fuzzy if-then...
    C. Moraga in Real World Applications of Computational Intelligence
    Chapter

  15. Bioinformatics with Evolutionary Computation

    This chapter makes the presumption that it is more important to understand the domain of the problem of interest and to pursue the best achievable...
    D. Howard in Real World Applications of Computational Intelligence
    Chapter

  16. A New Theoretical Framework for K-Means-Type Clustering

    One of the fundamental clustering problems is to assign n points into k clusters based on the minimal sum-of-squares(MSSC), which is known to be...
    J. Peng, Y. **a in Foundations and Advances in Data Mining
    Chapter

  17. Rough Set Theory with Applications to Data Mining

    This paper is an introduction to rough set theory with an emphasis on applications to data mining. First, consistent data are discussed, including...
    J.W. Grzymala-Busse in Real World Applications of Computational Intelligence
    Chapter

  18. Intelligent Agents to Improve Adaptivity in A Web-Based Learning Environment

    This chapter focuses on the use of intelligent agents in on-line learning environments in which educational organizations can equip students with...
    C. I. Peña, J. L. Marzo, J. Ll. de la Rosa in Knowledge-Based Virtual Education
    Chapter

  19. Intelligent Agents that Learn to Deliver Online Materials to Students Better: Agent Design, Simulation and Assumptions

    In this chapter, we discuss an integrated framework of case-based learning (CBL) in an agent that intelligently delivers learning materials to...
    Leen-Kiat Soh, Todd Blank, Lee Dee Miler in Knowledge-Based Virtual Education
    Chapter

  20. 5 The Production Cell Example

    This chapter explains how to model and control a benchmark example, the Production Cell, using the State Tree Structure (STS) methodology. We will...
    Chuan Ma, W. Murray Wonham in Nonblocking Supervisory Control of State Tree Structures
    Chapter
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