Efficient Second-Order Online Kernel Learning with Adaptive Embedding

NeurIPS 2017 Daniele CalandrielloAlessandro LazaricMichal Valko

Online kernel learning (OKL) is a flexible framework to approach prediction problems, since the large approximation space provided by reproducing kernel Hilbert spaces can contain an accurate function for the problem. Nonetheless, optimizing over this space is computationally expensive... (read more)

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