Titlebook: Artificial Neural Networks in Pattern Recognition; Second IAPR Workshop Friedhelm Schwenker,Simone Marinai Conference proceedings 2006 Spri

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Incremental Training of Support Vector Machines Using Truncated Hyperconespercones. We generate the truncated surface with the center being the center of unbounded support vectors and with the radius being the maximum distance from the center to support vectors. We determine the hypercone surface so that it includes a datum, which is far away from the separating hyperplan

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Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniqueslementation of decomposition techniques leads to infinite loops. To solve this problem and to further speed up training, in this paper, we propose an improved decomposition techniques for training LP-SVMs. If an infinite loop is detected, we include in the next working set all the data in the workin

Enthralling

Multiple Classifier Systems for Embedded String Patterns. However, there has been reported only little work on combining classifiers in structural pattern recognition. In this paper we describe a method for embedding strings into real vector spaces based on prototype selection, in order to gain several vectorial descriptions of the string data. We presen

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Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theorye used to retrieve the classification result. More complex ways of evaluating the hierarchy output that take into account the complete information the hierarchy provides yield improved classification results. Due to the hierarchical output space decomposition that is inherent to hierarchical neural

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Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalizationa single output. In this paper we focus on the combination module. We have proposed two methods based on . as the combination module of an ensemble of neural networks. In this paper we have performed a comparison among the two versions of . and six statistical combination methods in order to get the

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https://doi.org/10.1007/11829898artificial neural network; bioinformatics; cognition; data mining; learning; neural network; pattern recog

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978-3-540-37951-5Springer-Verlag GmbH Germany, part of Springer Nature 2006