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Hence, we propose the following patch-translation robust distance, denoted BD2: p ), dtr (GN , HN ) = min d(GN , HN p∈[1,P ] (3) where the superscript p stands for the translation of the first patch of GN source image onto the pth patch of HN source image, spatially warping around the other patches. 1 Results We propose to assess the performance of the HMMDM classifier on proportional data comparing it to HMMGM and NN classifiers. To the best of our knowledge, this work represents its first use on real-world data.
RD ), and β = (β1 ,. . ,βD ) denote the distribution mean, left standard deviation, right standard deviation, and the shape parameter for the D-dimensional AGGD, respectively. The parameter β controls the shape of the probability density function (pdf). The larger the value, the ﬂatter the pdf; and the smaller the value, the more picked the pdf. This means that β determines the decay rate of the density function. The AGGD is chosen to be able to ﬁt, in analytically simple and realistic way, symmetric or non-symmetric data by the combination of the left and right variances.
On the other hand, the lower part shows a reasonably good prediction obtained by recurrent neural network (MSE approximately equal to 3). Thus, according to the evaluation on the test data, one would chose 16 features and only 7 features as the ﬁnal number of inputs for feed-forward network or recurrent network, respectively. Third, the ﬁnal models were validated on separate validation set 2007/2008 and 2008/2009. This error can be found in the lower-left part of Figure 3. One can see that the comparison results for the two methods are the same for both the testing and validation data.