• alignment based kernel learning with a continuous set of base kernels

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 959
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     the success of kernel-based learning methods depends on the choice of kernel. recently, kernel learning methods have been proposed that use data to select the most appropriate kernel, usually by combining a set of base kernels. we introduce a new algorithm for kernel learning that combines a continuous set of base kernels, without the common step of discretizing the space of base kernels. we demonstrate that our new method achieves state-of-the-art performance across a variety of real-world datasets. furthermore, we explicitly demonstrate the importance of combining the right dictionary of kernels, which is problematic for methods that combine a finite set of base kernels chosen a priori. our method is not the first approach to work with continuously parameterized kernels. we adopt a two-stage kernel learning approach. we also show that our method requires substantially less computation than previous such approaches, and so is more amenable to multi-dimensional parameterizations of base kernels, which we demonstrate.

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