Thesis (Ph. D.)--University of Wisconsin, 1966. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Three forms of multivariate analysis, one very classical and the other two relatively new and little-known, are showcased and enhanced: the first is the Singular Value Decomposition (SVD), which is at the heart of many statistical, and now geostatistical, techniques; the second is the method of Variogram Analysis, which is one way of investigating spatial correlation in one or several variables; and the third is the process of interpolation known as cokriging, a method for optimizing the estimation of multivariate data based on the information provided through variogram analysis. The SVD is described in detail, and it is shown that the SVD can be generalized from its familiar matrix (two-dimensional) case to three, and possibly n, dimensions. This generalization we call the "Tensor SVD" (or TSVD), and we demonstrate useful applications in the field of geostatistics (and indicate ways in which it will be useful in other areas). Applications of the SVD to the tools of geostatistics are described: in particular, applications dependent on the TSVD, including variogram modelling in coregionalization. Variogram analysis in general is explored, and we propose broader use of an old tool (which we call the "corhogram ", based on the variogram) which proves useful in helping one choose variables for multivariate interpolation. The reasoning behind kriging and cokriging is discussed, and a better algorithm for solving the cokriging equations is developed, which results in simultaneous kriging estimates for comparison with those obtained from cokriging. Links from kriging systems to kernel systems are made; discovering kerneIs equivalent to kriging systems will be useful in the case where data are plentiful. Finally, some results of the application of geostatistical techniques to a data set concerning nitrate pollution in the West Salt River Valley of Arizona are described.

We study two different operational scenarios for a regional air ambulance service-company which has bases in Northern California. Two of these bases serve the land areas encompassed roughly in a circular area of radius 100 miles centered in Gilroy and Salinas, respectively; with a large part of their coverage areas reachable from either base. The base in Salinas currently operates one helicopter only from Thursday to Monday, whereas the base in Gilroy operates one helicopter 24/7. The company is considering extending the operation of one helicopter to 24/7 for its Salinas base. In this study we analyze the operational impacts of that extension, and develop a framework that can be applied towards the study of the ambulance assignment problem faced by small operators. / pa/cb Original. 10/06/05. updated 09/09/2011.

Chow Kai Tik = 支援矢量回歸法之影像表示式及其影像處理與分析 / 周啓迪. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 380-383). / Text in English; abstracts in English and Chinese. / Chow Kai Tik = Zhi yuan shi liang hui gui fa zhi ying xiang biao shi shi ji qi ying xiang chu li yu fen xi / Zhou Qidi. / Abstract in English / Abstract in Chinese / Acknowledgement / Content / List of figures / Chapter Chapter 1 --- Introduction --- p.1-11 / Chapter 1.1 --- Introduction --- p.2 / Chapter 1.2 --- Road Map --- p.9 / Chapter Chapter 2 --- Review of Support Vector Machine --- p.12-124 / Chapter 2.1 --- Structural Risk Minimization (SRM) --- p.13 / Chapter 2.1.1 --- Introduction / Chapter 2.1.2 --- Structural Risk Minimization / Chapter 2.2 --- Review of Support Vector Machine --- p.21 / Chapter 2.2.1 --- Review of Support Vector Classification / Chapter 2.2.2 --- Review of Support Vector Regression / Chapter 2.2.3 --- Review of Support Vector Clustering / Chapter 2.2.4 --- Summary of Support Vector Machines / Chapter 2.3 --- Implementation of Support Vector Machines --- p.60 / Chapter 2.3.1 --- Kernel Adatron for Support Vector Classification (KA-SVC) / Chapter 2.3.2 --- Kernel Adatron for Support Vector Regression (KA-SVR) / Chapter 2.3.3 --- Sequential Minimal Optimization for Support Vector Classification (SMO-SVC) / Chapter 2.3.4 --- Sequential Minimal Optimization for Support Vector Regression (SMO-SVR) / Chapter 2.3.5 --- Lagrangian Support Vector Classification (LSVC) / Chapter 2.3.6 --- Lagrangian Support Vector Regression (LSVR) / Chapter 2.4 --- Applications of Support Vector Machines --- p.117 / Chapter 2.4.1 --- Applications of Support Vector Classification / Chapter 2.4.2 --- Applications of Support Vector Regression / Chapter Chapter 3 --- Image Representation by Support Vector Regression --- p.125-183 / Chapter 3.1 --- Introduction of SVR Representation --- p.116 / Chapter 3.1.1 --- Image Representation by SVR / Chapter 3.1.2 --- Implicit Smoothing of SVR representation / Chapter 3.1.3 --- "Different Insensitivity, C value, Kernel and Kernel Parameters" / Chapter 3.2 --- Variation on Encoding Method [Training Process] --- p.154 / Chapter 3.2.1 --- Training SVR with Missing Data / Chapter 3.2.2 --- Training SVR with Image Blocks / Chapter 3.2.3 --- Training SVR with Other Variations / Chapter 3.3 --- Variation on Decoding Method [Testing pr Reconstruction Process] --- p.171 / Chapter 3.3.1 --- Reconstruction with Different Portion of Support Vectors / Chapter 3.3.2 --- Reconstruction with Different Support Vector Locations and Lagrange Multiplier Values / Chapter 3.3.3 --- Reconstruction with Different Kernels / Chapter 3.4 --- Feature Extraction --- p.177 / Chapter 3.4.1 --- Features on Simple Shape / Chapter 3.4.2 --- Invariant of Support Vector Features / Chapter Chapter 4 --- Mathematical and Physical Properties of SYR Representation --- p.184-243 / Chapter 4.1 --- Introduction of RBF Kernel --- p.185 / Chapter 4.2 --- Mathematical Properties: Integral Properties --- p.187 / Chapter 4.2.1 --- Integration of an SVR Image / Chapter 4.2.2 --- Fourier Transform of SVR Image (Hankel Transform of Kernel) / Chapter 4.2.3 --- Cross Correlation between SVR Images / Chapter 4.2.4 --- Convolution of SVR Images / Chapter 4.3 --- Mathematical Properties: Differential Properties --- p.219 / Chapter 4.3.1 --- Review of Differential Geometry / Chapter 4.3.2 --- Gradient of SVR Image / Chapter 4.3.3 --- Laplacian of SVR Image / Chapter 4.4 --- Physical Properties --- p.228 / Chapter 4.4.1 --- 7Transformation between Reconstructed Image and Lagrange Multipliers / Chapter 4.4.2 --- Relation between Original Image and SVR Approximation / Chapter 4.5 --- Appendix --- p.234 / Chapter 4.5.1 --- Hankel Transform for Common Functions / Chapter 4.5.2 --- Hankel Transform for RBF / Chapter 4.5.3 --- Integration of Gaussian / Chapter 4.5.4 --- Chain Rules for Differential Geometry / Chapter 4.5.5 --- Derivation of Gradient of RBF / Chapter 4.5.6 --- Derivation of Laplacian of RBF / Chapter Chapter 5 --- Image Processing in SVR Representation --- p.244-293 / Chapter 5.1 --- Introduction --- p.245 / Chapter 5.2 --- Geometric Transformation --- p.241 / Chapter 5.2.1 --- "Brightness, Contrast and Image Addition" / Chapter 5.2.2 --- Interpolation or Resampling / Chapter 5.2.3 --- Translation and Rotation / Chapter 5.2.4 --- Affine Transformation / Chapter 5.2.5 --- Transformation with Given Optical Flow / Chapter 5.2.6 --- A Brief Summary / Chapter 5.3 --- SVR Image Filtering --- p.261 / Chapter 5.3.1 --- Discrete Filtering in SVR Representation / Chapter 5.3.2 --- Continuous Filtering in SVR Representation / Chapter Chapter 6 --- Image Analysis in SVR Representation --- p.294-370 / Chapter 6.1 --- Contour Extraction --- p.295 / Chapter 6.1.1 --- Contour Tracing by Equi-potential Line [using Gradient] / Chapter 6.1.2 --- Contour Smoothing and Contour Feature Extraction / Chapter 6.2 --- Registration --- p.304 / Chapter 6.2.1 --- Registration using Cross Correlation / Chapter 6.2.2 --- Registration using Phase Correlation [Phase Shift in Fourier Transform] / Chapter 6.2.3 --- Analysis of the Two Methods for Registrationin SVR Domain / Chapter 6.3 --- Segmentation --- p.347 / Chapter 6.3.1 --- Segmentation by Contour Tracing / Chapter 6.3.2 --- Segmentation by Thresholding on Smoothed or Sharpened SVR Image / Chapter 6.3.3 --- Segmentation by Thresholding on SVR Approximation / Chapter 6.4 --- Appendix --- p.368 / Chapter Chapter 7 --- Conclusion --- p.371-379 / Chapter 7.1 --- Conclusion and contribution --- p.372 / Chapter 7.2 --- Future work --- p.378 / Reference --- p.380-383

Wu, Xiaoming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 30-34). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Preliminaries --- p.4 / Chapter 2.1 --- Kernel Learning Theory --- p.4 / Chapter 2.1.1 --- Positive Semidefinite Kernel --- p.4 / Chapter 2.1.2 --- The Reproducing Kernel Map --- p.6 / Chapter 2.1.3 --- Kernel Tricks --- p.7 / Chapter 2.2 --- Spectral Graph Theory --- p.8 / Chapter 2.2.1 --- Graph Laplacian --- p.8 / Chapter 2.2.2 --- Eigenvectors of Graph Laplacian --- p.9 / Chapter 2.3 --- Convex Optimization --- p.10 / Chapter 2.3.1 --- From Linear to Conic Programming --- p.11 / Chapter 2.3.2 --- Second-Order Cone Programming --- p.12 / Chapter 2.3.3 --- Semidefinite Programming --- p.12 / Chapter 3 --- Fast Graph Laplacian Regularized Kernel Learning --- p.14 / Chapter 3.1 --- The Problems --- p.14 / Chapter 3.1.1 --- MVU --- p.16 / Chapter 3.1.2 --- PCP --- p.17 / Chapter 3.1.3 --- Low-Rank Approximation: from SDP to QSDP --- p.18 / Chapter 3.2 --- Previous Approach: from QSDP to SDP --- p.20 / Chapter 3.3 --- Our Formulation: from QSDP to SQLP --- p.21 / Chapter 3.4 --- Experimental Results --- p.23 / Chapter 3.4.1 --- The Results --- p.25 / Chapter 4 --- Conclusion --- p.28 / Bibliography --- p.30

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Thesis (M.S.)--Ohio University, March, 2004. / Title from PDF t.p. Includes bibliographical references (leaves 51-55).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 88-98). Also available in electronic version.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2005. / Thesis Advisor(s): Roberto Szechtman. Includes bibliographical references (p. 67-70). Also available online.

Thesis (doctoral)--Universität Bonn, 2003. / Includes bibliographical references (p. 163-165).