1. Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning
- Author
-
David Nebel, Marika Kaden, Thomas Villmann, and Andrea Villmann
- Subjects
business.industry ,Cognitive Neuroscience ,Mathematical properties ,Pattern recognition ,02 engineering and technology ,Similarity measure ,Machine learning ,computer.software_genre ,Computer Science Applications ,03 medical and health sciences ,ComputingMethodologies_PATTERNRECOGNITION ,0302 clinical medicine ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Data pre-processing ,business ,Equivalence (measure theory) ,computer ,030217 neurology & neurosurgery ,Mathematics - Abstract
In this paper, we introduce taxonomies for similarity and dissimilarity measures, respectively, based on their mathematical properties. Further, we propose a definition for rank equivalence of (dis)similarities regarding given data for prototype based methods. Starting with this definition we provide a measure to judge the degree of equivalence, which can be used to compare respective measures as well as to consider the influence of data preprocessing regarding a single (dis)similarity measure. In the last part of the paper an adaptive mixture approach of (dis)similarity measures for improved classification learning is presented.
- Published
- 2017