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• W1525770147 abstract "We provide faster algorithms for the problem of Gaussian summation, which occurs in many machine learning methods. We develop two new extensions - an O(Dp) Taylor expansion for the Gaussian kernel with rigorous error bounds and a new error control scheme integrating any arbitrary approximation method - within the best discretealgorithmic framework using adaptive hierarchical data structures. We rigorously evaluate these techniques empirically in the context of optimal bandwidth selection in kernel density estimation, revealing the strengths and weaknesses of current state-of-the-art approaches for the first time. Our results demonstrate that the new error control scheme yields improved performance, whereas the series expansion approach is only effective in low dimensions (five or less)." @default.
• W1525770147 created "2016-06-24" @default.
• W1525770147 creator A5005255629 @default.
• W1525770147 creator A5033610642 @default.
• W1525770147 date "2012-06-27" @default.
• W1525770147 modified "2023-09-25" @default.
• W1525770147 title "Faster Gaussian Summation: Theory and Experiment" @default.
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• W1525770147 hasPublicationYear "2012" @default.
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