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Abstract: We proposed a method of incremental projection learning which provides exactly the same generalization capability as that obtained by batch projection learning in the previous paper. However, properties of the method have not yet been investigated. In this paper, we analyze its properties from the following aspects: First, it is shown that some of the training examples regarded as redundant in most incremental learning methods have potential e#ectiveness, i.e., they will contribute to... (Update)
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...#m 1 = PN(Am ) #m 1 , 24) # m 1 = #m 1 A # m t m 1 , 25) # m 1 = V m # m 1 . 26) As shown in Sugiyama and Ogawa [16, 18], the additional training examples such that # m 1 = 0 can be rejected since they have no e#ect on learning results. Hence, from here on, we...
...#m 1 = PN(Am ) #m 1 , 11) # m 1 = #m 1 A # m t m 1 , 12) # m 1 = V m # m 1 . 13) As shown in Sugiyama and Ogawa [7, 9], the additional training examples such that # m 1 = 0 can be rejected since they have no e#ect on learning results. Hence, from here on, we...
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BibTeX entry: (Update)
Sugiyama, M., & Ogawa, H. (1999c). Properties of incremental projection learning. Technical Report TR99-0008, Department of Computer Science, Tokyo Institute of Technology, Japan. (available at http://www.cs.titech.ac.jp/TR/tr99.html) http://citeseer.ist.psu.edu/sugiyama01properties.html More
@article{ sugiyama01properties,
author = "M. Sugiyama and H. Ogawa",
title = "Properties of incremental projection learning",
journal = "Neural Networks",
volume = "14",
number = "1",
pages = "53--66",
year = "2001",
url = "citeseer.ist.psu.edu/sugiyama01properties.html" }
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