Anja Struyf education supervisor

Research Anja Struyf

Research team

Development of time-efficient algorithms for depth functions based on techniques of computational geometry, and new applications to economic data. 01/10/2001 - 30/09/2005

Abstract

The first goal is to further investigate the notion of location depth, and to construct time-efficient algorithms to compute the depth and the corresponding contours and location estimator for a given data set in 2, 3, or more dimensions. It will also be attempted to extend this work to the equally important problem of estimating the scatter structure of the data. The second goal is to analyze and model economical systems, like inflation and trading on financial markets, by means of depth functions and other robust techniques.

Researcher(s)

Research team(s)

    Project type(s)

    • Research Project

    Algorithms for fuzzy classification. 01/10/1999 - 30/09/2001

    Abstract

    Automatic classification is the division of data into homogeneous subsets. This discipline belongs to computer science, pattern recognition, and statistics, with many applications. The goal of this project is the development of classification techniques which are robust towards the usual model assumptions. The research primarily concerns fuzzy set techniques, with a particular interest towards new methods with high contrast.

    Researcher(s)

    Research team(s)

      Project type(s)

      • Research Project

      Algorithms for fuzzy classification. 01/10/1997 - 30/09/1999

      Abstract

      Automatic classification is the division of data into homogeneous subsets. This discipline belongs to computer science, pattern recognition, and statistics, with many applications. The goal of this project is the development of classification techniques which are robust towards the usual model assumptions. The research primarily concerns fuzzy set techniques, with a particular interest towards new methods with high contrast.

      Researcher(s)

      Research team(s)

        Project type(s)

        • Research Project

        Algorithms for fuzzy classification. 01/10/1996 - 30/09/1997

        Abstract

        Automatic classification is the division of data into homogeneous subsets. This discipline belongs to computer science, pattern recognition, and statistics, with many applications. The goal of this project is the development of classification techniques which are robust towards the usual model assumptions. The research primarily concerns fuzzy set techniques, with a particular interest towards new methods with high contrast.

        Researcher(s)

        Research team(s)

          Project type(s)

          • Research Project