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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Application of Support Vector Machines in Inverse Problems in Ocean Color Remote Sensing,evaluate the performance of the proposed approach. Experimental results show that the SVM performs as well as the optimal multi-layer perceptron (MLP) and can be a promising alternative to the conventional MLPs for the retrieval of oceanic chlorophyll concentration from marine reflectance.

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1434-9922 erous real-world applications, such as bioinformatics, text .The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as

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Fuzzy Support Vector Machines with Automatic Membership Setting,to the decision surface. In our previous research, we applied a fuzzy membership to each input point and reformulate the support vector machines to be fuzzy support vector machines (FSVMs) such that different input points can make different contributions to the learning of the decision surface.

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Application of Support Vector Machine to the Detection of Delayed Gastric Emptying from Electrogastng 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 improve the performance of the conventional neural networks.

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Active Support Vector Learning with Statistical Queries,obust and efficient learning capabilities. The confidence factor is estimated from local information using the . nearest neighbor principle. Effectiveness of the method is demonstrated on real life data sets both in terms of generalization performance and training time.