Regularization

By Xin-Hong Zhang. Encyclopedia of Machine Learning, 2019

296 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1a9d448166963704e139ff90334a3247c373f42d

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Citations in the corpus

Anisotropic diffusion on sub-manifolds with application to Earth structure classification (2012, background): “Given the true parameters of Ȳ (namely, X̄), the re-parametrization error of a point y(i) ∈ Y is computed as the difference between its interpolated parameters values and its true parameters x(i) .”
Supplementary Material for Regularization on Spatio-Temporally Smoothed Feature for Action Recognition (2020, methodology): “Therefore, we use RMS with [1,3,3] kernel in CSN.”
Fractional differential equations: a novel study of local and global solutions in Banach spaces (2013, unknown): “This is a kind of convolution quite common in the theory of Volterra integral equations of first kind (see for instance [9]).”
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision (2021, unknown): “Recent unsupervised MRC studies mainly adopt data synthetization approachs (Yang and Mitchell, 2017; Dhingra et al., 2018; Pan et al., 2020; Hong et al., 2020; Kang et al., 2020).”
Unsupervised feature learning for audio classification using convolutional deep belief networks (2009, background): “[17, 18, 19, 20] This suggests that the first-layer CDBN features learned somewhat informative features for phone classification tasks in an unsupervised way.”
Edge and line oriented contour detection: State of the art (2011, background): “Important examples are affine-invariant scale spaces [57], morphological scale spaces [8,246,247], variational scale–spaces [245] and Lee-theoretic scale spaces [248], which are derived from nonlinear diffusion equations, and scale spaces derived from nonlinear wavelets [249–251].”
Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey (2019, unknown): “An optimal choice of ̂f for an arbitrary class F was introduced by Mendelson [62], and that choice is also based on median-of-means tournament, though a different tournament than the one defined in [59].”