Abstract
This paper explores collaborative ability of co-training algorithm. We propose a new measurement (CA) for representing the collaborative ability of co-training classifiers based on the overlapping proportion between certain and uncertain instances. The CA measurement indicates whether two classifiers can co-train effectively. We make theoretical analysis for CA values in co-training with independent feature split, with random feature split and without feature split. The experiments justify our analysis. We also explore two variations of the general co-training algorithm and analyze them using the CA measurement.
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© 2005 Springer-Verlag Berlin Heidelberg
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Shen, D., Zhang, J., Su, J., Zhou, G., Tan, CL. (2005). A Collaborative Ability Measurement for Co-training. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_46
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DOI: https://doi.org/10.1007/978-3-540-30211-7_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24475-2
Online ISBN: 978-3-540-30211-7
eBook Packages: Computer ScienceComputer Science (R0)