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Machine Learning
Published in Seyedeh Leili Mirtaheri, Reza Shahbazian, Machine Learning Theory to Applications, 2022
Seyedeh Leili Mirtaheri, Reza Shahbazian
Computational learning theory is a branch of theoretical computer science that focuses on the computational analysis of machine learning algorithms and their performance. In learning theory, probabilistic bounds on the performance of algorithms are pretty standard because of the uncertainly of the future and the limited training data sets. For example, the generalization error could quantify using the bias-variance decomposition.
AI Is One Thing and Many Things
Published in Tom Lawry, AI in Health, 2020
Machine learning evolved from the study of pattern recognition and computational learning theory. The term was coined by AI pioneer J.A.N. Lee in 1959, who defined it as a “field of study that gives computers the ability to learn without being explicitly programmed.2”
Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue
Published in International Journal of Computer Integrated Manufacturing, 2022
Tianyuan Liu, Jinsong Bao, Junliang Wang, Jiacheng Wang
Computational learning theory examines the theory of learning through computation. It aims to analyze the difficult nature of the learning tasks and provide theoretical assurance on the feasibility of the learning algorithm and to guide the design of the algorithm. The feasibility of DL algorithms can be discussed in the following three situations: the hypothesis space has only one hypothesis function, the hypothesis space has a limited number of hypothesis functions, and the hypothesis space has an infinite number of hypothesis functions (Zhou 2016). Only one hypothesis function