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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study the classification problems over string data for hypothesesspecified by formulas of monadic second-order logic MSO. The goal is to designlearning algorithms that run in time polynomial in the size of the trainingset, independently of or at least sublinear in the size of the whole data set.We prove negative as well as positive results. If the data set is anunprocessed string to which our algorithms have local access, then learning insublinear time is impossible even for hypotheses definable in a small fragmentof first-order logic. If we allow for a linear time pre-processing of thestring data to build an index data structure, then learning of MSO-definablehypotheses is possible in time polynomial in the size of the training set,independently of the size of the whole data set.

Learning MSO-definable hypotheses on string