26,941 research outputs found
Ensembles of wrappers for automated feature selection in fish age classification
In feature selection, the most important features must be chosen so as to decrease the number thereof while retaining their discriminatory information. Within this context, a novel feature selection method based on an ensemble of wrappers is proposed and applied for automatically select features in fish age classification. The effectiveness of this procedure using an Atlantic cod database has been tested for different powerful statistical learning classifiers. The subsets based on few features selected, e.g. otolith weight and fish weight, are particularly noticeable given current biological findings and practices in fishery research and the classification results obtained with them outperforms those of previous studies in which a manual feature selection was performed.Peer ReviewedPostprint (author's final draft
Transfer learning through greedy subset selection
We study the binary transfer learning problem, focusing on how to select sources from a large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer learning setting where one does not have direct access to the source data, but rather employs the source hypotheses trained from them. Building on the literature on the best subset selection problem, we propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously. On three computer vision datasets we achieve state-of-the-art results, substantially outperforming transfer learning and popular feature selection baselines in a small-sample setting. Also, we theoretically prove that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples
OCReP: An Optimally Conditioned Regularization for Pseudoinversion Based Neural Training
In this paper we consider the training of single hidden layer neural networks
by pseudoinversion, which, in spite of its popularity, is sometimes affected by
numerical instability issues. Regularization is known to be effective in such
cases, so that we introduce, in the framework of Tikhonov regularization, a
matricial reformulation of the problem which allows us to use the condition
number as a diagnostic tool for identification of instability. By imposing
well-conditioning requirements on the relevant matrices, our theoretical
analysis allows the identification of an optimal value for the regularization
parameter from the standpoint of stability. We compare with the value derived
by cross-validation for overfitting control and optimisation of the
generalization performance. We test our method for both regression and
classification tasks. The proposed method is quite effective in terms of
predictivity, often with some improvement on performance with respect to the
reference cases considered. This approach, due to analytical determination of
the regularization parameter, dramatically reduces the computational load
required by many other techniques.Comment: Published on Neural Network
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