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Kernel embedding of distributions

In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Heterogeneous Transfer Learning (HTL) aims to solve transfer learning problems where a source domain and a target domain are of… Expand
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2018
2018
We propose an unsupervised object matching method for relational data, which finds matchings between objects in different… Expand
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2017
2017
A required assumption of a Kalman filter, the most-widely-used state estimator in avionic systems, is the Gaussian and whiteness… Expand
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Highly Cited
2016
Highly Cited
2016
Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given… Expand
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2016
2016
The kernel embedding of distributions is a popular machine learning technique to manipulate probability distributions and is an… Expand
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2015
2015
We propose a new algorithm named EXPected Similarity Estimation (EXPoSE) to approach the problem of anomaly detection (also known… Expand
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2014
2014
Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert… Expand
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2014
2014
Kernel embedding of distributions maps distributions to the reproducing kernel Hilbert space (RKHS) of a kernel function, such… Expand
2013
2013
Kernel embedding of distributions has led to many recent advances in machine learning. However, latent and low rank structures… Expand
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