Kalvis Apsitis

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A FIN-learning machine M receives successive values of the function f it is learning and at some moment outputs a conjecture which should be a correct index of f. FIN learning has two extensions: (1) If M ips fair coins and learns a function with certain probability p, we have FINp-learning. (2) When n machines simultaneously try to learn the same function(More)
We combine traditional studies of inductive inference and classical continuous mathematics to produce a study of learning real-valued functions. We consider two possible ways to model the learning by example of functions with domain and range the real numbers. The first approach considers functions as represented by computable analytic functions. The second(More)