Results 221 to 230 of about 331,479,482 (252)
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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. +3 more
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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. +3 more
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Journal of Economic Theory, 1997
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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2007
As new graph structured data is constantly being generated, learning and data mining on graphs have become a challenge in application areas such as molecular biology, telecommunications, chemoinformatics, and social network analysis. The central algorithmic problem in these areas, measuring similarity of graphs, has therefore received extensive ...
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As new graph structured data is constantly being generated, learning and data mining on graphs have become a challenge in application areas such as molecular biology, telecommunications, chemoinformatics, and social network analysis. The central algorithmic problem in these areas, measuring similarity of graphs, has therefore received extensive ...
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Contrastive Multi-View Kernel Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Jiyuan Liu +2 more
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Simultaneous Global and Local Graph Structure Preserving for Multiple Kernel Clustering
IEEE Transactions on Neural Networks and Learning Systems, 2021Zhenwen Ren, Quansen Sun
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2013
Kernel methods have been used very successfully to classify data in various application domains. Traditionally, kernels have been constructed mainly for vectorial data defined on a specific vector space. Much less work has been addressing the development of kernel functions for non-vectorial data.
Baisero, Andrea +3 more
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Kernel methods have been used very successfully to classify data in various application domains. Traditionally, kernels have been constructed mainly for vectorial data defined on a specific vector space. Much less work has been addressing the development of kernel functions for non-vectorial data.
Baisero, Andrea +3 more
openaire +1 more source
Bridging deep and multiple kernel learning: A review
Information Fusion, 2021Tinghua Wang, Wenyu Hu
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SimpleMKKM: Simple Multiple Kernel K-Means
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Xinwang Liu
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Simple multiple kernel k-means with kernel weight regularization
Information Fusion, 2023Miaomiao Li, Yi Zhang, Suyuan Liu
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