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Correlation filters are special classifiers designed for shift-invariant object recognition, which are robust to pattern distortions. The recent literature shows that combining a set of sub-filters trained based on a single or a small group of images obtains the best performance. The idea is equivalent to estimating variable distribution based on the data sampling (bagging), which can be interpreted...
This study aimed at training artificial neural networks (ANN) to predict survival time in cancer patients by using microarray and clinical data. We analyzed public microarrays and clinical data sets in different kinds of cancer. We selected 15-30 genes (correlation coefficient>0.4) as ANN variables to train networks. The results shows ANN can predict survival time from Microarray data gene expression...
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