Probabilistic clustering is utilised to transform the multidimensional data into a onedimensional sequence of symbols which is then used to train a hidden Markov model classifier We present the results of an experiment where the learned classifier can predict a contact location with an accuracy of 97
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The algorithm is fast but not effective when applied to spiral curves To the best of our knowledge none of the algorithms of either approach reported in the literature have formally resolved the problem of approximation of thick curves as done in this paper 1998 2 C Liu H Wechsler A Gabor feature classifier for face recognition
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A twospiral data set has been chosen as it consists in delineating complex classification boundaries It is a very wellknown set taken as reference in many papers to show the ability of proposed classifiers This data set consists of 194 data points each having a dimension of 2 Fig 11 is visualized the Spiral
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Probabilistic clustering is utilised to transform the multidimensional data into a onedimensional sequence of symbols which is then used to train a hidden Markov model classifier We present the results of an experiment where the learned classifier can predict a contact location with an accuracy of 97
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The original FishNet algorithm 345 can be summarized as below Fishnet Learning Algorithm Input A training data set D with a set of class labels C P N Output An rule which is composed of contribution functions for each attribute a threshold and resultant headrope
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