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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The enhanced multi-weight vector projection support vector machine (EMVSVM) is an outstanding algorithm for binary classification, which is proposed recently. However, it measures the distances in an objective function by the squared <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-norm, which exaggerates the effects of outliers or noisy data. In order to alleviate this problem, we propose an effective novel EMVSVM, termed robust EMVSVM based on the L2,1-norm distance (L2,1-EMVSVM). The distances in the objective of our algorithm are measured by the L2,1-norm. Besides, a new powerful iterative algorithm is designed to solve the formulated objective, whose convergence is ensured by theoretical proofs. Finally, the effectiveness and robustness of L2,1-EMVSVM are verified through extensive experiments.

ieee access

Robust &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$L_{2,1}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Norm Distance Enhanced Multi-Weight Vector Projection Support Vector Machine