14 March 2018

Data Science: Generalization (Definitions)

"The ability of a neural net to produce reasonable responses to input patterns that are similar, but not identical, to training patterns. A balance between memorization and generalization is usually desired." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"The ability of an information system to process new, unknown input data in order to obtain the best possible solution, or one close to it." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"The ability of a neural computing system to generalize from the input/output examples it was trained on to produce a sensible output to a previously unseen input. Compromise of the variance-bias dilemma." (Guido J Deboeck and Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

"way of responding ill the same way to a class of inputs, some of which do not belong to the training set of the same class." (Teuvo Kohonen, "Self-Organizing Maps 3rd Ed.", 2001)

"The process of creating a model based on specific instances that is an acceptable predictor of other instances." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

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