English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper provides a general framework to study the effect of samplingproperties of training data on the generalization error of the learned machinelearning (ML) models. Specifically, we propose a new spectral analysis of thegeneralization error, expressed in terms of the power spectra of the samplingpattern and the function involved. The framework is build in the Euclideanspace using Fourier analysis and establishes a connection between some highdimensional geometric objects and optimal spectral form of differentstate-of-the-art sampling patterns. Subsequently, we estimate the expectederror bounds and convergence rate of different state-of-the-art samplingpatterns, as the number of samples and dimensions increase. We make severalobservations about generalization error which are valid irrespective of theapproximation scheme (or learning architecture) and training (or optimization)algorithms. Our result also sheds light on ways to formulate design principlesfor constructing optimal sampling methods for particular problems.

A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis