104,133 research outputs found
e-Distance Weighted Support Vector Regression
We propose a novel support vector regression approach called e-Distance
Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two
challenging issues in support vector regression: first, the process of noisy
data; second, how to deal with the situation when the distribution of boundary
data is different from that of the overall data. The proposed e-DWSVR optimizes
the minimum margin and the mean of functional margin simultaneously to tackle
these two issues. In addition, we use both dual coordinate descent (CD) and
averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable
to large scale problems. We report promising results obtained by e-DWSVR in
comparison with existing methods on several benchmark datasets
A Kernel Perspective for Regularizing Deep Neural Networks
We propose a new point of view for regularizing deep neural networks by using
the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm
cannot be computed, it admits upper and lower approximations leading to various
practical strategies. Specifically, this perspective (i) provides a common
umbrella for many existing regularization principles, including spectral norm
and gradient penalties, or adversarial training, (ii) leads to new effective
regularization penalties, and (iii) suggests hybrid strategies combining lower
and upper bounds to get better approximations of the RKHS norm. We
experimentally show this approach to be effective when learning on small
datasets, or to obtain adversarially robust models.Comment: ICM
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