Results 211 to 220 of about 331,479,482 (252)
Kernel Factory: An ensemble of kernel machines [PDF]
Expert Systems with Applications, 2013 We propose an ensemble method for kernel machines. The training data is randomly split into a number of mutually exclusive partitions defined by a row and column parameter. Each partition forms an input space and is transformed by an automatically selected kernel function into a kernel matrix K.
Michel Ballings, Dirk Van den Poel
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2011 International Conference on Computer Vision, 2011
Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization.
Tinne Tuytelaars +3 more
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Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization.
Tinne Tuytelaars +3 more
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Partitionable Kernels for Mapping Kernels
2011 IEEE 11th International Conference on Data Mining, 2011Many of tree kernels in the literature are designed tanking advantage of the mapping kernel framework. The most important advantage of using this framework is that we have a strong theorem to examine positive definiteness of the resulting tree kernels.
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Multiple kernel clustering with corrupted kernels
Neurocomputing, 2017Abstract Multiple kernel clustering (MKC) algorithms usually learn an optimal kernel from a group of pre-specified base kernels to improve the clustering performance. However, we observe that existing MKC algorithms do not well handle the situation that kernels are corrupted with noise and outliers.
Teng Li 0010 +4 more
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Reliable Computing, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Hierarchical kernels in deep kernel learning
J. Mach. Learn. Res., 2023Summary: Kernel methods are built upon the mathematical theory of reproducing kernels and reproducing kernel Hilbert spaces. They enjoy good interpretability thanks to the solid mathematical foundation. Recently, motivated by deep neural networks in deep learning, which construct learning functions by successive compositions of activation functions and
Wentao Huang, Houbao Lu, Haizhang Zhang
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Communications of the ACM, 2018
Choosing between programming in the kernel or in user space.
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Choosing between programming in the kernel or in user space.
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Proceedings of the May 6-10, 1974, national computer conference and exposition on - AFIPS '74, 1974
Recent experience in computer security has illustrated the susceptibility of numerous operating systems to hostile penetration. Successful penetrations have been directed at manufacturers' conventional operating systems as well as special "secure" versions that have been the subjects of exhaustive efforts to find and fix all potential security problems.
Steven B. Lipner +6 more
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Recent experience in computer security has illustrated the susceptibility of numerous operating systems to hostile penetration. Successful penetrations have been directed at manufacturers' conventional operating systems as well as special "secure" versions that have been the subjects of exhaustive efforts to find and fix all potential security problems.
Steven B. Lipner +6 more
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Computer Graphics Forum, 2012
AbstractWe introduce the medial kernel, an association measure which provides for a robust construction of volume‐aware distances defined directly on point clouds. The medial kernel is a similarity measure defined as the likelihood of two points belonging to a common interior medial ball. We use the medial kernel to construct a random walk on the point
Matthew Berger, Cláudio T. Silva
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AbstractWe introduce the medial kernel, an association measure which provides for a robust construction of volume‐aware distances defined directly on point clouds. The medial kernel is a similarity measure defined as the likelihood of two points belonging to a common interior medial ball. We use the medial kernel to construct a random walk on the point
Matthew Berger, Cláudio T. Silva
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18th International Conference on Pattern Recognition (ICPR'06), 2006
In this work we introduce a new methodology to build a kernel matrix from a collection of kernels. The key idea is to build an unique kernel that eliminates spurious differences between kernels. We propose a method based on the Procrustes problems that uses the Alternating Projections method to minimize a certain error measure.
Isaac Martín de Diego, Alberto Muñoz
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In this work we introduce a new methodology to build a kernel matrix from a collection of kernels. The key idea is to build an unique kernel that eliminates spurious differences between kernels. We propose a method based on the Procrustes problems that uses the Alternating Projections method to minimize a certain error measure.
Isaac Martín de Diego, Alberto Muñoz
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