We are improving our search experience. To check which content you have full access to, or for advanced search, go back to the old search.

Search

Please fill in this field.
Filters applied:
Computer science Artificial intelligence Computer imaging, vision, pattern recognition and graphics

Search Results

Showing 81-100 of 10,000 results

  1. Clustering with Intelligent Techniques

    Cluster analysis is a technique for grou** data and finding structures in data. The most common application of clustering methods is to partition a...
    Patricia Melin, Oscar Castillo in Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
    Chapter

  2. New Trends of Develo** Hybrid Intelligent Systems – AIS Hybridization and DNA-Hybridization

    As already mentioned in Chap.1, the use of biologically inspired CI techniques play a crucial role for the hybridisation at any level of HIS features...
    Mircea Gh. Negoita, Daniel Neagu, Vasile Palade in Computational Intelligence
    Chapter

  3. Componentwise Least Squares Support Vector Machines

    This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of...
    K. Pelckmans, I. Goethals, ... B.D. Moor in Support Vector Machines: Theory and Applications
    Chapter

  4. Support Vector Machines for Signal Processing

    This chapter deals with the use of the support vector machine (SVM) algorithm as a possible design method in the signal processing applications. It...
    D. Mattera in Support Vector Machines: Theory and Applications
    Chapter

  5. Theoretical and Practical Model Selection Methods for Support Vector Classifiers

    In this chapter, we revise several methods for SVM model selection, deriving from different approaches: some of them build on practical lines of...
    D. Anguita, A. Boni, ... D. Sterpi in Support Vector Machines: Theory and Applications
    Chapter

  6. Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification

    Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions due to...
    J. Peng, D.R. Heisterkamp, H.K. Dai in Support Vector Machines: Theory and Applications
    Chapter

  7. The Artificial Mind System (AMS), Modules, and Their Interactions

    The previous two chapters have been devoted to establishing the novel artificial neural network concept, namely the kernel memory concept, for the...
    Tetsuya Hoya in Artificial Mind System - Kernel Memory Approach
    Chapter

  8. Genetic Algorithms and Genetic Linkage

    This chapter provides a summary of fundamental materials on genetic algorithms. It presents definitions of genetic algorithm terms and briefly...
    Ying-** Chen in Extending the Scalability of Linkage Learning Genetic Algorithms
    Chapter

  9. Incremental Mining on Association Rules

    The discovery of association rules has been known to be useful in selective marketing, decision analysis, and business management. An important...
    W.-G. Teng, M.-S. Chen in Foundations and Advances in Data Mining
    Chapter

  10. Fuzzy Support Vector Machines with Automatic Membership Setting

    Support vector machines like other classification approaches aim to learn the decision surface from the input points for classification problems or...
    C.-fu Lin, S.-de Wang in Support Vector Machines: Theory and Applications
    Chapter

  11. Epilogue – Towards Develo** A Realistic Sense of Artificial Intelligence

    So far, we have considered how the artificial mind system based upon the holistic model as depicted in Fig. 5.1 (on page 84) works in terms of the...
    Tetsuya Hoya in Artificial Mind System - Kernel Memory Approach
    Chapter

  12. Memory Modules and the Innate Structure

    As the philosopher Miguel de Umamuno (1864-1936) once said, “We live in memory and memory, and our spiritual life is at bottom simply the effort of...
    Tetsuya Hoya in Artificial Mind System - Kernel Memory Approach
    Chapter

  13. Genetic Linkage Learning Techniques

    The importance of learning genetic linkage has been discussed in the previous chapter and recognized in the field of genetic and evolutionary...
    Ying-** Chen in Extending the Scalability of Linkage Learning Genetic Algorithms
    Chapter

  14. A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set

    A correct selection of features (attributes) is vital in data mining. For this aim, the complete set of features is constructed. Here are some...
    T.Y. Lin in Foundations and Advances in Data Mining
    Chapter

  15. Web Mining – Concepts, Applications and Research Directions

    From its very beginning, the potential of extracting valuable knowledge from the Web has been quite evident. Web mining, i.e. the application of data...
    T. Srivastava, P. Desikan, V. Kumar in Foundations and Advances in Data Mining
    Chapter

  16. Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets

    We propose the use of maximal frequent itemsets (MFIs) to derive association rules from tabular datasets. We first present an efficient method to...
    Q. Zou, Y. Chen, ... X. Lu in Foundations and Advances in Data Mining
    Chapter

  17. CONCLUDING REMARKS

    The choice of problems presented in this study is intended to emphasize that in some cases even the classical problems of acoustics can be addressed...
    Bożena Kostek in Perception-Based Data Processing in Acoustics
    Chapter

  18. Fuzzy Subgroups of Abelian Groups

    Some of the best examples of algebraic structure theory come from commutative group theory. Commutative group theory is also a principal reason for...
    John N. Mordeson, Kiran R. Bhutani, Azriel Rosenfeld in Fuzzy Group Theory
    Chapter

  19. A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle

    In molecular biology, functions are produced by a set of macromolecules that interact at different levels. Genes and their products, proteins,...
    Florence d’Alché-Buc, Pierre-Jean Lahaye, ... Samuele Bottani in Bioinformatics Using Computational Intelligence Paradigms
    Chapter

  20. A Comparative Investigation on Model Selection in Binary Factor Analysis

    Binary factor analysis has been widely used in data analysis with various applications. Most studies assume a known hidden factors number k or...
    Yujia An, Xuelei Hu, Lei Xu in Foundations of Data Mining and knowledge Discovery
    Chapter
Did you find what you were looking for? Share feedback.