On Interpretability and Similarity in Concept-Based Machine Learning

Autor:	Dmitry I. Ignatov, Léonard Kwuida
Rok vydání:	2021
Předmět:	Computer science business.industry Concept learning Similarity (psychology) Artificial intelligence Transparency (human–computer interaction) business Machine learning computer.software_genre Cluster analysis Class (biology) computer Interpretability
Zdroj:	Lecture Notes in Computer Science ISBN: 9783030726096 AIST
DOI:	10.1007/978-3-030-72610-2_3
Popis:	Machine Learning (ML) provides important techniques for classification and predictions. Most of these are black-box models for users and do not provide decision-makers with an explanation. For the sake of transparency or more validity of decisions, the need to develop explainable/interpretable ML-methods is gaining more and more importance. Certain questions need to be addressed: How does an ML procedure derive the class for a particular entity? Why does a particular clustering emerge from a particular unsupervised ML procedure? What can we do if the number of attributes is very large? What are the possible reasons for the mistakes for concrete cases and models?
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fca31012e98043945252721ea755154a https://doi.org/10.1007/978-3-030-72610-2_3 Zobrazit plný text záznamu