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An appreciable fraction of introns is thought to be involved in cellular functions, but there is no obvious way to predict which specific intron is likely to be functional. For each intron we are given a feature representation that is based on its evolutionary patterns. For a small subsets of introns we are also given an indication that they are functional. For all other introns it is not known whether...
In this study we address the problem of training a neural network for language identification using speech samples in the form of i-vectors. Our approach involves training a classifier and analyzing the obtained confusion matrix. We cluster the languages by simultaneously clustering the columns and the rows of the confusion matrix. The language clusters are then used to define a modified cost function...
In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. We describe a deep-learning classification method that is based...
In this study we address the problem of training a neural network based on data with unreliable labels. We introduce an extra noise layer by assuming that the observed labels were created from the true labels by passing through a noisy channel whose parameters are unknown. We propose a method that simultaneously learns both the neural network parameters and the noise distribution. The proposed method...
Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we apply a multi-view-classifier for the task. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic...
In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single malignant or benign decision...
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