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

Transductive SVM (TSVM) is a well known semi-supervised large margin learningmethod for binary text classification. In this paper we extend this method tomulti-class and hierarchical classification problems. We point out that thedetermination of labels of unlabeled examples with fixed classifier weights isa linear programming problem. We devise an efficient technique for solving it.The method is applicable to general loss functions. We demonstrate the value ofthe new method using large margin loss on a number of multi-class andhierarchical classification datasets. For maxent loss we show empirically thatour method is better than expectation regularization/constraint and posteriorregularization methods, and competitive with the version of entropyregularization method which uses label constraints.

Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses