Title: | Overfitting in feature selection: Pitfalls and solutions Ylisovittuminen piirrevalinnassa: sudenkuoppia ja ratkaisuja |
Author(s): | Reunanen, Juha |
Date: | 2012 |
Language: | en |
Pages: | 136 |
Department: | Tietojenkäsittelytieteen laitos Department of Information and Computer Science |
ISBN: | 978-952-60-4516-0 (electronic) 978-952-60-4515-3 (printed) |
Series: | Aalto University publication series DOCTORAL DISSERTATIONS, 19/2012 |
ISSN: | 1799-4942 (electronic) 1799-4934 (printed) 1799-4934 (ISSN-L) |
Supervising professor(s): | Simula, Olli, Prof |
Thesis advisor(s): | Corona, Francesco, Dr |
Subject: | Computer science |
Keywords: | machine learning, feature selection, variable selection, overfitting, search algorithms, comparison of algorithms, model selection, classification, regression, koneoppiminen, piirrevalinta, muuttujien valinta, ylisovittuminen, hakualgoritmit, algoritmien vertailu, mallin valinta, luokittelu, regressio |
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Abstract:Hahmontunnistustehtävää ratkaistaessa määritetään yleensä joukko piirteitä eli ominaisuuksia, jotka kuvaavat tunnistettavia kohteita. Luokittelijan rakentavan algoritmin tehtävä on löytää näihin piirteisiin perustuvat säännöt, jotka soveltuvat uusien kohteiden luokitteluun. |
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Parts:[Publication 1]: Juha Reunanen (2003). Overfitting in Making Comparisons Between Variable Selection Methods. Journal of Machine Learning Research, volume 3, pp. 1371-1382.[Publication 2]: Juha Reunanen (2004). A Pitfall in Determining the Optimal Feature Subset Size. In A. Fred (Ed.), Proceedings of the Fourth International Workshop on Pattern Recognition in Information Systems (PRIS 2004), Porto, Portugal, April 13-14, pp. 176-185.[Publication 3]: Juha Reunanen (2006). Less Biased Measurement of Feature Selection Benefits. In C. Saunders, M. Grobelnik, S. Gunn, J. Shawe-Taylor (Eds.), Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop (SLSFS 2005), Revised Selected Papers (LNCS 3940), pp. 198-208.[Publication 4]: Juha Reunanen (2007). Model Selection and Assessment Using Cross-indexing. In J. Si, R. Sun, D. Brown, I. King, N. Kasabov (Eds.), Proceedings of the Twentieth International Joint Conference on Neural Networks (IJCNN 2007), Orlando, Florida, USA, August 12-17, pp. 2581-2585.[Publication 5]: Juha Reunanen, Mika Mononen, Marko Vauhkonen, Anssi Lehikoinen, and Jari P. Kaipio (2011). Machine Learning Approach for Locating Phase Interfaces Using Conductivity Probes. Inverse Problems in Science and Engineering, volume 19, issue 6, pp. 879-902. |
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