Majid Beigi

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We consider a class of random process signals which contain randomly position local similarities representing the texture of an object. Those repetitive parts may occur in speech, musical pieces and sonar signals. We suggest a warped time resolved spectrum kernel for extracting the subsequence similarity in time series in general, and as an example in(More)
Many classifiers are designed with the assumption of well-balanced datasets. But in real problems, like protein classification and remote homology detection, when using binary classifiers like support vector machine (SVM) and kernel methods, we are facing imbalanced data in which we have a low number of protein sequences as positive data (minor class)(More)
— This paper addresses the problem of object classification in a biosonar based mobile robot in a natural environment using a boosting method. We present an algorithm based on gradient boosting for biosanar-based robots that recognize different objects such as different trees via reflected sonar echoes. Gradient boosting is a machine learning approach, that(More)
G-protein coupled receptors (GPCRs) are a large superfam-ily of integral membrane proteins that transduce signals across the cell membrane. Because of that important property and other physiological roles undertaken by the GPCR family, they have been an important target of therapeutic drugs. The function of many GPCRs is not known and accurate(More)
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