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- W2045162610 abstract "Abstract Multiple Kernel Learning (MKL) aims to seek a better result than single kernel learning by combining a compact set of sub-kernels. However, MKL with L1-norm easily discards the sub-kernels with complementary information and MKL with Lp - norm ( p ≥ 2 ) often gets the redundant solution. To address these problems, a Selective Multiple Kernel Learning (SMKL) method, inspired by Ensemble Learning (EL), is proposed. Comparing MKL with Lp - norm ( p ≥ 2 ) , SMKL obtains a sparse solution by a pre-selection procedure. Comparing MKL with L1-norm, SMKL preserves the sub-kernels with complementary information by guaranteeing the high discrimination and large diversity of pre-selected sub-kernels. For quantifying the discrimination and diversity of sub-kernels, a new kernel evaluation is designed. SMKL reduces the scale of MKL optimization and saves the memory storing of the sub-kernels, which extends the scale of problem that MKL could solve. Specially, a fast SMKL method using L ∞ - norm constraint is focused, which needs no MKL optimization process. It means that the memory is hardly a limitation for MKL with the large scale problem. Experiments state that our method is effective for classification." @default.
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- W2045162610 date "2013-11-01" @default.
- W2045162610 modified "2023-10-18" @default.
- W2045162610 title "Selective multiple kernel learning for classification with ensemble strategy" @default.
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- W2045162610 doi "https://doi.org/10.1016/j.patcog.2013.04.003" @default.
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