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

Machine learning and data analysis have been used in many robotics fields,especially for modelling. Data are usually the result of sensor measurementsand, as such, they might be subjected to noise and outliers. The presence ofoutliers has a huge impact on modelling the acquired data, resulting ininappropriate models. In this work a novel approach for outlier detection andrejection for input/output mapping in regression problems is presented. Therobustness of the method is shown both through simulated data for linear andnonlinear regression, and real sensory data. Despite being validated by usingartificial neural networks, the method can be generalized to any otherregression method

A Robust Regression Approach for Robot Model Learning