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

We propose new methods for Support Vector Machines (SVMs) using treearchitecture for multi-class classi- fication. In each node of the tree, weselect an appropriate binary classifier using entropy and generalization errorestimation, then group the examples into positive and negative classes based onthe selected classi- fier and train a new classifier for use in theclassification phase. The proposed methods can work in time complexity betweenO(log2N) to O(N) where N is the number of classes. We compared the performanceof our proposed methods to the traditional techniques on the UCI machinelearning repository using 10-fold cross-validation. The experimental resultsshow that our proposed methods are very useful for the problems that need fastclassification time or problems with a large number of classes as the proposedmethods run much faster than the traditional techniques but still providecomparable accuracy.

Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation