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Analisi matematica. Vol. 2 by Enrico Giusti

By Enrico Giusti

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The second step aims to the selection of the real nodules within the set of the extracted candidates; this is generally done by means of classification methods, which first reduce the number of candidates, usually quite high, and then extract the final ones. The main issue regarding this step is the choice of a proper set of features to describe and represent the data so that the samples belonging to the two classes may easily distinguished. , 1993], [A. Schilham and Loog, 2003]); in both cases however many true positives are discarded, leaving the problem open.

According to the uniform distribution. Then the data of the original training set are projected to the selected n-dimensional subspaces and the resulting data sets are used to train an ensemble of learning machines [Ho, 1998]. Feature selection combined with random subspace ensemble 31 RS-SF Algorithm Input: - A data set D = {(xj , tj )|1 ≤ j ≤ m}, xj ∈ X ⊂ Rd , tj ∈ C = {1, . . , k} - a learning algorithm L - a feature selection algorithm F - a number of selected features n < d - a dimension k < n of the random subspace - number of the base learners I Output: - Final hypothesis hran : X → C computed by the ensemble.

Segments were described by 23-dimensional feature vectors including the average multiple sequence alignment profile, the relative index of one strand and its normalized start and end amino acid positions. In these experiments, we Protein Structure Assembly from Knowledge of β -sheet Motifs 51 Figure 2. Histograms showing the distribution of RMSD (left) and GDT TS (right) on the set of reconstructed structures using native and predicted β-sheets topologies. applied a 5-fold cross validation procedure.

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