Moderate deviation and restricted equivalence functions for measuring similarity between data
| Autor: | Edurne Barrenechea, Miguel Pagola, Zdenko Takáč, Juan I. Forcen, Abdulrahman H. Altalhi, Humberto Bustince |
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| Přispěvatelé: | Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas, Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Cities, Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila |
| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
05 social sciences Restricted equivalence function 050301 education Deviation 02 engineering and technology Moderate deviation function Penalty function Score matrix Computer Science Applications Theoretical Computer Science Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing Penalty method Aggregation function Moderate deviations 0503 education Equivalence (measure theory) Software Mathematics |
| Zdroj: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra instname |
| Popis: | In this work we study the relation between moderate deviation functions, restricted dissimilarity functions and restricted equivalence functions. We use moderate deviation functions in order to measure the similarity or dissimilarity between a given set of data. We show an application of moderate deviate functions used in the same way as penalty functions to make a final decision from a score matrix in a classification problem. This work was supported in part by the Spanish Ministry of Science and Technology under project TIN2016-77356-P (AEI / FEDER, UE) and by grant VEGA 1/0614/18 . |
| Databáze: | OpenAIRE |
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