Promotion
发表于 2025-3-26 21:56:45
C. Niederau,P. G. Lankisch,S. Müller-Lissnert provide different resolution may be used at the same time, and difficult problems that require highly complex decision borders may be solved in a simple way. Relation of this approach to Support Vector Machines and Liquid State Machines is discussed.
Geyser
发表于 2025-3-27 04:14:20
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arbovirus
发表于 2025-3-27 06:21:41
Convergence Improvement of Active Set Training for Support Vector Regressorss paper, we discuss convergence improvement by modifying the training method. To stabilize convergence for a large epsilon tube, we calculate the bias term according to the signs of the previous variables, not the updated variables. And to speed up calculating the inverse matrix by the Cholesky fact
outskirts
发表于 2025-3-27 12:40:25
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legacy
发表于 2025-3-27 14:57:53
Support Vector Machines-Kernel Algorithms for the Estimation of the Water Supply in Cyprus the development of an .-Regression Support Vector Machine (SVMR) system with five input parameters. The 5-Fold Cross Validation method was applied in order to produce a more representative training data set. The fuzzy-weighted SVR combined with a fuzzy partition approach was employed in order to en
FECK
发表于 2025-3-27 19:26:44
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reject
发表于 2025-3-28 01:49:39
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值得尊敬
发表于 2025-3-28 04:04:54
A New Tree Kernel Based on SOM-SDs of methods have their own drawbacks. Kernels typically can only cope with discrete labels and tend to be sparse. On the other side, SOM-SD, an extension of the SOM for structured data, is unsupervised and Markovian, i.e. the representation of a subtree does not consider where the subtree appears i
Glucose
发表于 2025-3-28 06:39:10
Kernel-Based Learning from Infinite Dimensional 2-Way Tensorshere input data have a natural 2 −way representation, such as images or multivariate time series. Our approach aims at relaxing linearity of standard tensor-based analysis while still exploiting the structural information embodied in the input data.
纠缠
发表于 2025-3-28 12:10:07
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