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High-performance Kernel Machines with Implicit Distributed Optimization and Randomization
[article]
2015
arXiv
pre-print
In order to fully utilize "big data", it is often required to use "big models". Such models tend to grow with the complexity and size of the training data, and do not make strong parametric assumptions upfront on the nature of the underlying statistical dependencies. Kernel methods fit this need well, as they constitute a versatile and principled statistical methodology for solving a wide range of non-parametric modelling problems. However, their high computational costs (in storage and time)
arXiv:1409.0940v3
fatcat:dw3lzspvmnbyhouwgieqeij6sy