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Conversely, machine learning (ML) paradigms allow to tackle the quality assessment task from a different perspective, as the eventual goal is to mimic quality perception instead of designing an explicit model the human visual system. Several studies already proved the ability of ML-based approaches to address visual quality assessment; nevertheless, these paradigms are highly prone to overfitting, and their overall reliability may be questionable. In fact, a prerequisite for successfully using ML in modeling perceptual mechanisms is a profound understanding of the advantages and limitations that characterize learning machines. This paper illustrates and exemplifies the good practices to be followed.<\/jats:p>","DOI":"10.1186\/1687-5281-2013-54","type":"journal-article","created":{"date-parts":[[2013,9,23]],"date-time":"2013-09-23T13:40:57Z","timestamp":1379943657000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Supporting visual quality assessment with machine learning"],"prefix":"10.1186","volume":"2013","author":[{"given":"Paolo","family":"Gastaldo","sequence":"first","affiliation":[]},{"given":"Rodolfo","family":"Zunino","sequence":"additional","affiliation":[]},{"given":"Judith","family":"Redi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,9,23]]},"reference":[{"issue":"7","key":"97_CR1","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.image.2010.05.009","volume":"25","author":"SS Hemami","year":"2010","unstructured":"Hemami SS, Reibman AR: No-reference image and video quality estimation: applications and human-motivated design. 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