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

The training phase is the most important stage during the machine learningprocess. In the case of labeled data and supervised learning, machine trainingconsists in minimizing the loss function subject to different constraints. Inan abstract setting, it can be formulated as a multiple criteria optimizationmodel in which each criterion measures the distance between the outputassociated with a specific input and its label. Therefore, the fitting term isa vector function and its minimization is intended in the Pareto sense. Weprovide stability results of the efficient solutions with respect toperturbations of input and output data. We then extend the same approach to thecase of learning with multiple datasets. The multiple dataset environment isrelevant when reducing the bias due to the choice of a specific training set.We propose a scalarization approach to implement this model and numericalexperiments in digit classification using MNIST data.

Mixing Deep Learning and Multiple Criteria Optimization: An Application to Distributed Learning with Multiple Datasets