Data Science: Learning (Definitions)

"Procedures for modifying the weights on the connection links in a neural net (also known as training algorithms, learning rules)." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"In the simplest form: self-adaptation at the processing element level. Weighted connections between processing elements or weights are adjusted to achieve specific results, eliminating the need for writing a specific algorithm for each problem. More generally: change of rules or behavior for a certain objective." (Guido J Deboeck and Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

"generic name for all behavioral changes that depend on experiences and improve the performance of a system. In a more restricted sense learning is identical with adaptation, especially selective modification of parameters of a system." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"A process whereby a training set of examples is used to generate a model that understands and generalizes the relationship between the descriptor variables and one or more response variables." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"The process of automatically finding relations between inputs and outputs given examples of that relation." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"An essential operation of acquiring, processing and storing information required by any intelligent system for evolution." (T R Gopalakrishnan Nair, "Cognitive Approaches for Intelligent Networks", 2015)

"Adaptation of synaptic weights of a neural network as training progresses, usually with the objective of minimizing a cost function." (Anand Parey & Amandeep S Ahuja, "Application of Artificial Intelligence to Gearbox Fault Diagnosis: A Review", 2016)

"Algorithm for changing the parameters of a function based on examples. Learning algorithms are said to be “supervised” when both inputs and desired outputs are given or “unsupervised” when only inputs are given. Reinforcement learning is a special case of a supervised learning algorithm when the only feedback is a reward for good performance." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"A phase in the machine learning methods that aggregates some information about the state actions for using in the future predictions of the events." (Derya Yiltas-Kaplan, "The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks", 2019)