Abstract
This paper presents a method, called Adaptive Directed Acyclic Graph (ADAG), to extend Support Vector Machines (SVMs) for multiclass classification. The ADAG is based on the previous approach, the Decision Directed Acyclic Graph (DDAG), and is designed to remedy some weakness of the DDAG caused by its structure. We prove that the expected accuracy of the ADAG is higher than that of the DDAG, and also empirically evaluate our approach by comparing the ADAG with the DDAG on two data sets.
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© 2002 Springer-Verlag Berlin Heidelberg
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Kijsirikul, B., Ussivakul, N., Meknavin, S. (2002). Adaptive Directed Acyclic Graphs for Multiclass Classification. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_19
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DOI: https://doi.org/10.1007/3-540-45683-X_19
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