The use of group equivariant operators is becoming more and more important in machine learning and topological data analysis. In this paper we introduce a new method to build G-equivariant non-expansive operators from a set ϕ of bounded and continuous functions (Formula Presented) itself, where X is a topological space and G is a subgroup of the group of all self-homeomorphisms of X.

On a new method to build group equivariant operators by means of permutants / Camporesi, Francesco; Frosini, Patrizio; Quercioli, Nicola*. - STAMPA. - 11015:(2018), pp. 265-272. (Intervento presentato al convegno 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018 tenutosi a Hamburg, Germany nel 2018) [10.1007/978-3-319-99740-7_18].

On a new method to build group equivariant operators by means of permutants

Frosini, Patrizio;Quercioli, Nicola
2018

Abstract

The use of group equivariant operators is becoming more and more important in machine learning and topological data analysis. In this paper we introduce a new method to build G-equivariant non-expansive operators from a set ϕ of bounded and continuous functions (Formula Presented) itself, where X is a topological space and G is a subgroup of the group of all self-homeomorphisms of X.
2018
MACHINE LEARNING AND KNOWLEDGE EXTRACTION, CD-MAKE 2018
265
272
On a new method to build group equivariant operators by means of permutants / Camporesi, Francesco; Frosini, Patrizio; Quercioli, Nicola*. - STAMPA. - 11015:(2018), pp. 265-272. (Intervento presentato al convegno 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018 tenutosi a Hamburg, Germany nel 2018) [10.1007/978-3-319-99740-7_18].
Camporesi, Francesco; Frosini, Patrizio; Quercioli, Nicola*
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/657771
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