1,721,929 research outputs found
Pamela R. Fletcher
No full textPamela R. Fletcher
Associate Professorhttps://sophia.stkate.edu/catherineportrait/1026/thumbnail.jp
Letter from R. Fletcher to Robertson Topp
No full textLetter from R. Fletcher describing the Marble Mantles that he has found for the Gayoso Hotel
Norman R. Fletcher is Sheriff
No full textNorman R. Fletcher was voted in on the Democratic Party ticket as the new Uintah County Sheriff. He was sworn into office January 1959 and was sheriff until 1971
Instructions to R. Fletcher for purchasing certain articles for Gayoso
No full textInstructions from Robertson Topp to R. Fletcher for purchasing certain articles for Gayos
Letter from Horton & Macy detailing a contract with R. Fletcher
No full textLetter from Horton & Macy detailing a contract with R. Fletcher. Sent to Robertson Topp from Cincinnati
Binary separation and training support vector machines
No full textWe introduce basic ideas of binary separation by a linear hyperplane, which is a technique exploited in the support vector machine (SVM) concept. This is a decision-making tool for pattern recognition and related problems. We describe a fundamental standard problem (SP) and show how this is used in most existing research to develop a dual-based algorithm for its solution. This algorithm is shown to be deficient in certain aspects, and we develop a new primal-based SQP-like algorithm, which has some interesting features. Most practical SVM problems are not adequately handled by a linear hyperplane. We describe the nonlinear SVM technique, which enables a nonlinear separating surface to be computed, and we propose a new primal algorithm based on the use of low-rank Cholesky factors. It may be, however, that exact separation is not desirable due to the presence of uncertain or mislabelled data. Dealing with this situation is the main challenge in developing suitable algorithms. Existing dual-based algorithms use the idea of L1 penalties, which has merit. We suggest how penalties can be incorporated into a primal-based algorithm. Another aspect of practical SVM problems is often the huge size of the data set, which poses severe challenges both for software package development and for control of ill-conditioning. We illustrate some of these issues with numerical experiments on a range of problems
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