Results 211 to 220 of about 554,174 (252)

The NBNN kernel

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, Mario Fritz, Kate Saenko, Trevor Darrell   +3 more
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Partitionable Kernels for Mapping Kernels

2011 IEEE 11th International Conference on Data Mining, 2011
Many 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, 2017
Abstract 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, Yong Dou, Xinwang Liu 0002, Yang Zhao 0003, Qi Lv   +4 more
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Popping kernels

Communications of the ACM, 2018
Choosing between programming in the kernel or in user space.
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On Kernel Inclusions

Reliable Computing, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Hierarchical kernels in deep kernel learning

J. Mach. Learn. Res., 2023
Summary: 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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Semisupervised Kernel Matrix Learning by Kernel Propagation

IEEE Transactions on Neural Networks, 2010
The goal of semisupervised kernel matrix learning (SS-KML) is to learn a kernel matrix on all the given samples on which just a little supervised information, such as class label or pairwise constraint, is provided. Despite extensive research, the performance of SS-KML still leaves some space for improvement in terms of effectiveness and efficiency ...
Enliang Hu, Songcan Chen, Daoqiang Zhang, Xuesong Yin   +3 more
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Kernel Procrustes

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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Reinterpreting the Kernel

Journal of Economic Theory, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Kernel QuantTree.

2023
We present Kernel QuantTree (KQT), a nonparametric change detection algorithm that monitors multivariate data through a histogram. KQT constructs a nonlinear partition of the input space that matches pre-defined target probabilities and specifically promotes compact bins adhering to the data distribution, resulting in a powerful detection algorithm. We
Stucchi D., Rizzo P., Folloni N., Boracchi G.   +3 more
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