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In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. In contrast to SVMs, FMs model all interactions between variables using factorized parameters. Thus they are able to estimate interactions even...
Through statistic analysis of vibration and temperature signals of motor on the container crane hoisting mechanism in Waigaoqiao port, the feature vectors with vibration and temperature are obtained. Through data preprocessing and training data, Training models of condition parameters based on support vector machine (SVM) are established. The testing data of condition monitoring parameters can be...
Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of predictive (diagnostic) models using patients' data from clinical studies requires effective integration of genetic, clinical and demographic data. Typically all heterogeneous inputs are properly encoded and mapped onto a single feature vector, used for estimating (training) a predictive model....
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