In this paper we present an experimental study of machine learning and image analysis for performing automated morphological galaxy classification. We used a neural network, and a locally weighted regression method, and implemented homogeneous ensembles of classifiers. The ensemble of neural networks was created using the bagging ensemble method, and manipulation of input features was used to create the ensemble of locally weighed regression. The galaxies used were rotated, centred, and cropped, all in a fully automatic manner. In addition, we used principal component analysis to reduce the dimensionality of the data, and to extract relevant information in the images. Preliminary experimental results using 10‐fold cross‐validation show that the homogeneous ensemble of locally weighted regression produces the best results, with over 91 per cent accuracy when considering three galaxy types (E, S and Irr), and over 95 per cent accuracy for two types (E and S).

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We report the degree of order of twenty-two Jackson Pollock's paintings using Hausdorff-Besicovitch fractal dimension. Through the maximum value of each multi-fractal spectrum, the artworks are classify by the year in which they were painted. It has been reported that Pollock's paintings are fractal and it increased on his latest works. However our results show that fractal dimension of the paintings are on a range of fractal dimension with values close to two.We identify this behavior as a fractal-order transition. Based on the study of disorder-order transition in physical systems, we interpreted the fractal-order transition through its dark paint strokes in Pollocks' paintings, as structured lines following a power law measured by fractal dimension. We obtain selfsimilarity in some specific Pollock's paintings, that reveal an important dependence on the scale of observation. We also characterize by its fractal spectrum, the called Teri's Find. We obtained similar spectrums between Teri's Find and Number 5 from Pollock, suggesting that fractal dimension cannot be completely rejected as a quantitative parameter to authenticate this kind of artworks.

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