Sparse Kernel Density Construction Using Orthogonal Forward Regression With Leave-One-Out Test Score and Local Regularization
نویسندگان
چکیده
منابع مشابه
Automatic Kernel Regression Modelling Using Combined Leave-One-Out Test Score and Regularised Orthogonal Least Squares
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Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is i...
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Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the...
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ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
سال: 2004
ISSN: 1083-4419
DOI: 10.1109/tsmcb.2004.828199