A comparison of two dissimilarity functions for mixed-type predictor variables in the \(\delta\)-machine

DOI10.1007/s11634-021-00463-6OpenAlexW3200808408MaRDI QIDQ6161660

Mark de Rooij, Willem J. Heiser, Beibei Yuan

Publication date: 27 June 2023

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-021-00463-6

zbMATH Keywords

Monte Carlomixed-type datanonlinear classificationdissimilarity

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Statistical learning from a regression perspective
• Concept learning and feature selection based on square-error clustering
• Support-vector networks
• ROS regression: integrating regularization with optimal scaling regression
• The \(\delta \)-machine: classification based on distances towards prototypes
• Modern multidimensional scaling. Theory and applications.
• 10.1162/15324430260185592
• The Dissimilarity Representation for Pattern Recognition
• Finding Groups in Data
• An Introduction to Statistical Learning
• Random forests
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: A comparison of two dissimilarity functions for mixed-type predictor variables in the \(\delta\)-machine