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part of 2nd place solution NN, public lb private lb from portailhandicap.info import Dense, Dropout, Embedding, Flatten, Input, merge from .. light from an intuitive point of view on how does predicting one set of features using other two sets of features (with XGB) contributed to a better NN model? Hello After spending hours and hours on net in order to find best pano head for me, finally I decided that NN could be the solution. Because NN offers a lot of various pieces and systems I&#;m wondering which one would be the best for my purpose. My main goal is to produce spherical panoramas tours.

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The graph shows that for all data sets the k-nn model with local metric induction reduces the classification error significantly in comparision to the classical k-nn based on a global metric. In case of the data sets chess and splice the reduction is between 20% and 40% depending on the neighborhood size and in case of the. See figure: 'Performance of the training and testing sets of NN model for the compressive strength ' from publication 'Modeling the mechanical properties of rubberized concretes by neural network and genetic programming' on ResearchGate, the professional network for scientists.


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