The Experts below are selected from a list of 20568 Experts worldwide ranked by ideXlab platform

Nikolay V. Baranovskiy - One of the best experts on this subject based on the ideXlab platform.

Andrey S. Solodkin - One of the best experts on this subject based on the ideXlab platform.

Manabu Ichino - One of the best experts on this subject based on the ideXlab platform.

  • Similarity and Dissimilarity Measures for Mixed Feature-type Symbolic Data
    Springer Proceedings in Mathematics & Statistics, 2018
    Co-Authors: Manabu Ichino
    Abstract:

    This paper presents preliminary results for the similarity and dissimilaritymeasures based on the Cartesian System Model (CSM) that is a mathematical modelto manipulate mixed feature-type symbolic data. We define the notion of conceptsize for the description of each object in the feature space. By extending the notionto the concept sizes of the Cartesian join and the Cartesian meet of the descriptionsfor objects, we can obtain various similarity and dissimilarity measures. We presentespecially the asymmetric similarity measure, and the symmetric similarity anddissimilarity measures useful for pattern recognition problems.

  • Feature Clustering Method to Detect Monotonic Chain Structures in Symbolic Data
    Selected Contributions in Data Analysis and Classification, 2007
    Co-Authors: Manabu Ichino
    Abstract:

    Finding a linear structure in multidimensional data is a main purpose of the principal component analysis (PCA). This paper describes a feature clustering method to detect monotonic chain structures embedded in symbolic data tables based on the Cartesian System model (CSM) which is a mathematical model to manipulate symbolic objects.

  • Symbolic Pattern Classifiers Based on the Cartesian System Model
    Studies in Classification Data Analysis and Knowledge Organization, 1998
    Co-Authors: Manabu Ichino, Hiroyuki Yaguchi
    Abstract:

    As symbolic pattern classifiers, this paper presents region oriented methods based on the Cartesian System model which is a mathematical model to treat symbolic data. Our region oriented methods are able to use locally effective information to discriminate between pattern classes. This fact may achieve, at least superficially, a perfect discrimination of the pattern classes under a finite design set. Therefore, we have to take a ballance between the separability between classes and the generality of class desciptions. We describe this viewpoint theoretically and experimentally in order to assert the importance of feature selection which is essentially important in any pattern classification problem. We present also an example based on symbolic data in order to illustrate the usefulness of our approach.

Larry G Mastin - One of the best experts on this subject based on the ideXlab platform.

  • ash3d a finite volume conservative numerical model for ash transport and tephra deposition
    Journal of Geophysical Research, 2012
    Co-Authors: Hans F Schwaiger, Roger P Denlinger, Larry G Mastin
    Abstract:

    [1] We develop a transient, 3-D Eulerian model (Ash3d) to predict airborne volcanic ash concentration and tephra deposition during volcanic eruptions. This model simulates downwind advection, turbulent diffusion, and settling of ash injected into the atmosphere by a volcanic eruption column. Ash advection is calculated using time-varying pre-existing wind data and a robust, high-order, finite-volume method. Our routine is mass-conservative and uses the coordinate System of the wind data, either a Cartesian System local to the volcano or a global spherical System for the Earth. Volcanic ash is specified with an arbitrary number of grain sizes, which affects the fall velocity, distribution and duration of transport. Above the source volcano, the vertical mass distribution with elevation is calculated using a Suzuki distribution for a given plume height, eruptive volume, and eruption duration. Multiple eruptions separated in time may be included in a single simulation. We test the model using analytical solutions for transport. Comparisons of the predicted and observed ash distributions for the 18 August 1992 eruption of Mt. Spurr in Alaska demonstrate to the efficacy and efficiency of the routine.

Alexandr A. Stuparenko - One of the best experts on this subject based on the ideXlab platform.