The objective of KMM-LM is to maximize the geometric soft margin, and minimize the empirical classification error together with the domain discrepancy based on ...
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What does by a large margin mean?
What is kernel mean matching?
The objective of KMM-LM is to maximize the geometric soft margin, and minimize the empirical classification error together with the domain discrepancy based on ...
KMM was extended by leveraging the class label knowledge and integrated KMM and SVM into an unified optimization framework called KMM-LM (Large Margin), ...
Kernel Mean Matching with a Large Margin. https://doi.org/10.1007/978-3-642-35527-1_19 ·. Journal: Advanced Data Mining and Applications Lecture Notes in ...
Abstract. Various instance weighting methods have been proposed for instance-based transfer learning. Kernel Mean Matching (KMM) is one.
The Kernel Mean Matching (KMM) is an elegant algorithm that produces density ratios between training and test data by minimizing their maximum mean ...
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Oct 21, 2023 · The MMD measures the difference in the means of the feature maps of the source and target data in a reproducing kernel Hilbert space (RKHS).
Missing: Large Margin.
We achieve this goal by matching covariate distributions between training and test sets in a high dimensional feature space (specifically, a reproducing kernel ...
Focusing on a particular co- variate shift problem, we derive high proba- bility confidence bounds for the kernel mean matching (KMM) estimator, whose conver-.
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Kernel Mean Matching (KMM) is a well known method for estimating density ratio, but has time complexity cubic in the size of training data.
Missing: Margin. | Show results with:Margin.