Titlebook: Support Vector Machines: Theory and Applications; Lipo Wang Book 2005 Springer-Verlag Berlin Heidelberg 2005 Data Mining.Fuzzy.Kernel Mach

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Kernel Discriminant Learning with Application to Face Recognition,from two problems: (1) small training sample size compared to the dimensionality of the sample (or mapped kernel feature) space, and (2) high computational complexity. In this chapter, we introduce a new kernel discriminant learning method, which attempts to deal with the two problems by using regul

有恶意

Fast Color Texture-Based Object Detection in Images: Application to License Plate Localization,l in the image into object of interest and background based on localized color texture patterns. The main problem in this approach is high run-time complexity of SVMs. To alleviate this problem, two methods are proposed. Firstly, an artificial neural network (ANN) is adopted to make the problem line

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Support Vector Machines for Signal Processing,itically discusses the main difficulties related with its application to such a general set of problems. Moreover, the problem of digital channel equalization is also discussed in details since it is an important example of the use of the SVM algorithm in the signal processing.

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Cancer Diagnosis and Protein Secondary Structure Prediction Using Support Vector Machines,and protein secondary structure prediction (PSSP). For the problem of cancer diagnosis, the SVMs that we used achieved highly accurate results with fewer genes compared to previously proposed approaches. For the problem of PSSP, the SVMs achieved results comparable to those obtained by other methods

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Application of Support Vector Machines in Inverse Problems in Ocean Color Remote Sensing,h as the danger of over-fitting and may easily be trapped in a local minimum. This paper investigates the possibility of using a new universal approximator, support vector machine (SVM), as the nonlinear transfer function in inverse problem in ocean color remote sensing. A field data set is used to

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Application of Support Vector Machine to the Detection of Delayed Gastric Emptying from Electrogasttudies reported neural network approaches for the non-invasive diagnosis of delayed gastric emptying from the cutaneous electrogastrograms (EGGs). Using support vector machines, we show that this relatively new technique can be used for detection of delayed gastric emptying and is in fact able to im

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Active-Set Methods for Support Vector Machines,tioned, or if high precision is needed. Algorithms are derived for classification and regression with both fixed and variable bias term. The material is completed by acceleration and approximation techniques as well as a comparison with other optimization methods in application examples.

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