Search for dissertations about: "non-parametric density estimators"

Found 3 swedish dissertations containing the words non-parametric density estimators.

  2. 1. Efficient Image Retrieval with Statistical Color Descriptors

    Author : Linh Viet Tran; Reiner Lenz; Björn Kruse; Linköpings universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; color properties; images; statistical; content-based image retrieval CBIR ; non-parametric density estimators; image database; Kernel; Gram-Schmidt; geometry-based; Information technology; Informationsteknologi;

    Abstract : Color has been widely used in content-based image retrieval (CBIR) applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. READ MORE

  4. 2. Continuous-Time Models in Kernel Smoothing

    Author : Martin Sköld; Matematisk statistik; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; deconvolution; errors-in-variables; continuous time; dependent data; bandwidth selection; asymptotic variance; Density estimation; kernel smoothing; size bias.; Mathematics; Matematik;

    Abstract : This thesis consists of five papers (Papers A-E) treating problems in non-parametric statistics, especially methods of kernel smoothing applied to density estimation for stochastic processes (Papers A-D) and regression analysis (Paper E). A recurrent theme is to, instead of treating highly positively correlated data as ``asymptotically independent'', take advantage of local dependence structures by using continuous-time models. READ MORE

  5. 3. On Symmetries and Metrics in Geometric Inference

    Author : Giovanni Luca Marchetti; Danica Kragic; Anastasiia Varava; Emanuele Rodolà; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Computational Geometry; Voronoi; Delaunay; Symmetry; Equivariance; Datalogi; Computer Science;

    Abstract : Spaces of data naturally carry intrinsic geometry. Statistics and machine learning can leverage on this rich structure in order to achieve efficiency and semantic generalization. Extracting geometry from data is therefore a fundamental challenge which by itself defines a statistical, computational and unsupervised learning problem. READ MORE