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

In this work we provide a theoretical framework for structured predictionthat generalizes the existing theory of surrogate methods for binary andmulticlass classification based on estimating conditional probabilities withsmooth convex surrogates (e.g. logistic regression). The theory relies on anatural characterization of structural properties of the task loss and allowsto derive statistical guarantees for many widely used methods in the context ofmultilabeling, ranking, ordinal regression and graph matching. In particular,we characterize the smooth convex surrogates compatible with a given task lossin terms of a suitable Bregman divergence composed with a link function. Thisallows to derive tight bounds for the calibration function and to obtain novelresults on existing surrogate frameworks for structured prediction such asconditional random fields and quadratic surrogates.

A General Theory for Structured Prediction with Smooth Convex Surrogates