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Almost Linear Biobasis Function Neural Networks
You, Liwen, Rögnvaldsson, Thorsteinn
Konferensbidrag (Övrigt vetenskapligt)
An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse.
Nyckelord: biobasis function neural networks; biological data set; biology computing; data analysis; distance measure linearity; distance measure normalization; linear classifiers; linear function; neural nets; pattern classification; similarity metric