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A continuous-valued infinite relational model is proposed as a solution to the co-clustering problem which arises in matrix data or tensor data calculations. The model is a probabilistic model utilizing the framework of Bayesian Nonparametrics which can estimate the number of components in posterior distributions. The original Infinite Relational Model cannot handle continuous-valued or multi-dimensional...
Maintaining situation awareness is a fundamental yet challenging task. In many domains, it's not the lack of data that complicates this task. Instead, the challenge is that relevant information is oftentimes obscured by the shear volume of available data, data that in some cases is incomplete or incorrect. For such domains, it may be difficult to quickly identify and adjudicate relevant information...
Missing data is a given in the medical domain, so machine learning models should have satisfactory performance even when missing data occurs. Our previous work has focused on support vector machines (SVM), but we hypothesize that Bayesian networks (BN) can handle missing data better. To test the hypothesis, we trained a BN and SVM model for 2 year survival on 322 lung cancer patients and compared...
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