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/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The capacity of a learning machine is measured by its Vapnik-Chervonenkisdimension, and learning machines with a low VC dimension generalize better. Itis well known that the VC dimension of SVMs can be very large or unbounded,even though they generally yield state-of-the-art learning performance. In thispaper, we show how to learn a hyperplane regressor by minimizing an exact, or\boldmath{$\Theta$} bound on its VC dimension. The proposed approach, termed asthe Minimal Complexity Machine (MCM) Regressor, involves solving a simplelinear programming problem. Experimental results show, that on a number ofbenchmark datasets, the proposed approach yields regressors with error ratesmuch less than those obtained with conventional SVM regresssors, while oftenusing fewer support vectors. On some benchmark datasets, the number of supportvectors is less than one tenth the number used by SVMs, indicating that the MCMdoes indeed learn simpler representations.

Learning a hyperplane regressor by minimizing an exact bound on the VC dimension