期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 47, 期 10, 页码 2653-2662出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2694321
关键词
Fastfood; minimal complexity machines (MCMs); random Fourier features; stochastic gradient descent; Vapnik-Chervonenkis (VC) dimension
资金
- Indian Institute of Technology Delhi (IIT Delhi) through the Microsoft Chair Professor Project [MI01158]
Minimal complexity machines (MCMs) are a class of hyperplane classifiers that try to minimize a tight bound on the Vapnik-Chervonenkis dimension. MCMs can be used both in the input space and in a higher dimensional feature space via the kernel trick. MCMs tend to produce very sparse solutions in comparison to support vector machines, often using three to ten times fewer support vectors. However, large datasets present significant challenges in terms of storage and operations on the kernel matrix. In this paper, we present a stochastic subgradient descent solver for large-scale machine learning with the MCM. The proposed approach uses an explicit feature map-based approximation of the kernel, to improve the scalability of the algorithm.
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