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

The problem of supervised classification (or discrimination) with functionaldata is considered, with a special interest on the popular k-nearest neighbors(k-NN) classifier. First, relying on a recent result by Cerou and Guyader(2006), we prove the consistency of the k-NN classifier for functional datawhose distribution belongs to a broad family of Gaussian processes withtriangular covariance functions. Second, on a more practical side, we check thebehavior of the k-NN method when compared with a few other functionalclassifiers. This is carried out through a small simulation study and theanalysis of several real functional data sets. While no global "uniform" winneremerges from such comparisons, the overall performance of the k-NN method,together with its sound intuitive motivation and relative simplicity, suggeststhat it could represent a reasonable benchmark for the classification problemwith functional data.

Supervised functional classification: A theoretical remark and some comparisons