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Beeman, N., Webb, P

Examples of learning statistical classification systems include, but are not limited to, using: inductive learning (e.g., decision/classification trees such as random forests, classification and regression trees (C & RT), boosting trees, etc.), approximate correct (PAC) learning, connection learning (e.g., Neural Networks (NN), Artificial Neural Networks (ANN), Neural Fuzzy Networks (NFN), network structures, perceptrons (e.g., multi-layer perceptrons), multi-layer feed-forward networks, neural network applications, Bayesian learning in belief networks, etc.), reinforcement learning (e.g., passive learning in known environments (e.g., initial learning, adaptive dynamic learning, and time-difference learning), passive learning in unknown environments, active learning in unknown environments, learning action value functions, reinforcement learning applications, etc.), and genetic algorithms and evolutionary planning

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Any observed clinical changes in trials are specific to those study designs and should not be generalized as established therapeutic effects outside a regulated research context