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 Plus

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

Zendy Tools

Zendy Open

This paper deals with the convergence analysis of the SUCPA (SemiUnsupervised Calibration through Prior Adaptation) algorithm, defined from afirst-order non-linear difference equations, first developed to correct thescores output by a supervised machine learning classifier. The convergenceanalysis is addressed as a dynamical system problem, by studying the local andglobal stability of the nonlinear map derived from the algorithm. This map,which is defined by a composition of exponential and rational functions, turnsout to be non-hyperbolic with a non-bounded set of non-isolated fixed points.Hence, a non-standard method for solving the convergence analysis is usedconsisting of an ad-hoc geometrical approach. For a binary classificationproblem (two-dimensional map), we rigorously prove that the map is globallyasymptotically stable. Numerical experiments on real-world application areperformed to support the theoretical results by means of two differentclassification problems: Sentiment Polarity performed with a Large LanguageModel and Cat-Dog Image classification. For a greater number of classes, thenumerical evidence shows the same behavior of the algorithm, and this isillustrated with a Natural Language Inference example. The experiment codes arepublicly accessible online at the following repository:https://github.com/LautaroEst/sucpa-convergence

On the Stability of a non-hyperbolic nonlinear map with non-bounded set of non-isolated fixed points with applications to Machine Learning