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With decision tree learning, a decision tree can be used as a predictive model, which can map observations about one or more parameters to conclusions about the parameter's target value. With association learning, relations between variables or parameters can be identified in large databases. With ANNs, computations can be structured through an interconnected group of artificial neurons, processing information using a connected approach. ANNs can be non-linear data modeling took, using various statistical methods and approaches known in the art. Deep learning can employ multiple layers in an artificial neural network. Inductive logic programming (ILP) can utilize logic programming for rule learning, e.g. using a uniform representation for input examples, background knowledge, and hypotheses. Support vector machines (SVMs) can be a set of supervised learning methods used for classification and/or regression. A given set of training examples can be marked as belonging to a first category or a second category; an SVM training machine can build a model predicting whether a new input falls into the first or the second category.

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