Algorithmic learning theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and algorithmic inductive inference. Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis. Both algorithmic and statistical learning theory are concerned with machine learning and can thus be viewed as branches of computational learning theory.
Read more about Algorithmic Learning Theory: Distinguishing Characteristics, Learning in The Limit, Other Identification Criteria
Famous quotes containing the words learning and/or theory:
“The child does not begin to fall until she becomes seriously interested in walking, until she actually begins learning. Falling is thus more an indication of learning than a sign of failure.”
—Polly Berrien Berends (20th century)
“It makes no sense to say what the objects of a theory are,
beyond saying how to interpret or reinterpret that theory in another.”
—Willard Van Orman Quine (b. 1908)