Interpretable support vector machines for functional data
Year of publication: |
2014
|
---|---|
Authors: | Martin-Barragan, Belen ; Lillo, Rosa ; Romo, Juan |
Published in: |
European journal of operational research : EJOR. - Amsterdam : Elsevier, ISSN 0377-2217, ZDB-ID 243003-4. - Vol. 232.2014, 1 (1.1.), p. 146-155
|
Subject: | Data mining | Interpretability | Classification | Linear programming | Regularization methods | Functional data analysis | Data Mining | Mustererkennung | Pattern recognition | Mathematische Optimierung | Mathematical programming | Prognoseverfahren | Forecasting model | Theorie | Theory | Statistische Methode | Statistical method | Klassifikation |
-
Depth-based support vector classifiers to detect data nests of rare events
Dyckerhoff, Rainer, (2021)
-
Robust classification via support vector machines
Asimit, Alexandru V., (2022)
-
Predictive analytics with strategically missing data
Zhang, Juheng, (2020)
- More ...
-
Interpretable support vector machines for functional data
Martin-Barragan, Belen, (2014)
-
Identifiability of the MAP <Subscript>2</Subscript>/G/1 queueing system
RamÃrez-Cobo, Pepa, (2014)
-
Handwritten digit classification
Giuliodori, Andrea, (2011)
- More ...