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Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of 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 a classifier. This approach, known as standard...
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....
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed general approach to formalizing such problems, known as learning...
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