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MicroRNAs (miRNAs) are small, non-coding RNAs which are involved in the posttranscriptional modulation of gene expression. Their short (18–24) single stranded mature sequences are involved in targeting specific genes. It turns out that experimental methods are limited and that it is difficult, if not impossible, to establish all miRNAs and their targets experimentally. Therefore, many tools for the...
This paper presents an effective feature selection method for support vector machine (SVM). Unlike the traditional combinatorial searching method, feature selection is translated into the model selection of SVM which has been well studied. In more detail, the basic idea of this method is to tune the parameters of the Gaussian ARD (Automatic Relevance Determination) kernel via optimization of kernel...
Feature subset selection is a well studied problem in machine learning. One short-coming of many methods is the selection of highly correlated features; a characteristic of hyperspec-tral data. A novel stochastic feature selection method with three major components is presented. First, we present an optimized feature selection method that maximizes a heuristic using a simulated annealing search which...
In a world where automation of processes is more and more on demand, machine vision is continuously explored to address several industrial problems such as quality inspection. In the processed-food industry where the external quality attributes of the product are inspected visually before the packaging line, machine vision systems often involve the extraction of a larger number of features than those...
Feature selection is a process commonly used in machine learning, wherein a subset of the features available from the data are selected for application of a learning algorithm. Feature selection is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy and efficiency. In this paper, we propose a new information distance to measure the relevancy of two features...
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