Subfields Of Sciences As Inspiration For Machine Learning Algorithms/Paradigms Perceptrons and Neural NETWORKS were inspired by Models of Neuron of the brain. So Neuroscience is obviously a major inspiration. Genetic Algorithms, Genetic Programming, Evolutionary Algorithms are inspired by Genetics and Evolutionary THEORY. Simulated annealing [1] Algorithm was invented for solving problems in Statistical PHYSICS and later used in Optimization problems in Artificial Intelligence and Machine Learning. Reinforcement Learning was first studied in Psychology, more specifically in BEHAVIORAL Psychology. Now Reinforcement Learning is a branch of Machine Learning. Statistics is the field most closely tied with Machine Learning apart from COMPUTERScience. Many Regression and Clustering techniques from Statistics act as inspiration to Machine Learning Algorithms. Bayesian Models, (Hidden) Markov Models were first studied as part of Probability Theory. The STUDY of Logic acts as the basis for many Knowledge Based Machine Learning Paradigms: Explanation Based Learning Relevance Based Learning Inductive Logic Programming
Posted on: Wed, 13 Aug 2014 04:53:54 +0000
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