Abstract
We have proposed a fast learning and classification method by using distributed representation of vectors. In this paper, first, we shows that our method provides faster and better performance than 1-NN method by introducing a definition of a similarity concerned with LSH scheme. Next we compare our method with the Naive Bayes with respect to the number of dimensions of features. While the Naive Bayes requires a considerably large dimensional feature space, our method achieves higher performance even where the number of dimensions of a feature space of our method is much smaller than that of Naive Bayes. We explain our method by formalizing as a linear classifier in a very high dimensional space and show it is a special case of Naive Bayes model. Experimental results show that our method provides superior classification rates with small time complexity of learning and classification and is applicable to large data set.
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© 2009 Springer-Verlag Berlin Heidelberg
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Kobayashi, T., Shimizu, I. (2009). A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_11
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DOI: https://doi.org/10.1007/978-3-642-03070-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
Online ISBN: 978-3-642-03070-3
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