This group gathers pioneers of computing methodologies whose primary contribution was building systems that improve their own performance from data or experience rather than following only fixed rules, including statistical and deep learning, artificial neural networks and backpropagation, reinforcement learning, computational and probably approximately correct learning theory, evolutionary computation such as genetic algorithms and genetic programming, and the data science and bioinformatics disciplines that apply these learning techniques to new domains. It follows the Machine learning strand of the ACM Computing Classification System's Artificial intelligence area under Computing methodologies. Their shared work is a system that learns from examples, as distinct from a hand engineered reasoning system, a vision or language specific application of learning, or an autonomous physical device.
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Machine Learning
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1. Wikipedia: Machine learning
WikipediaMachine learning, History section, Arthur Samuel sentenceQuote, Machine learning, History section, Arthur Samuel sentence
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.
View the Source Evelyn Fix (Wikipedia)
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the k-Nearest Neighbor (k-NN) algorithm
View the Source Leo Breiman (Wikipedia)
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His most important contributions were his work on classification and regression trees, and ensembles of trees fit to bootstrap samples.
View the Source Thomas M. Cover (Wikipedia)
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authored over 120 journal papers in learning, information theory, statistical complexity, pattern recognition, and portfolio theory
View the Source John Platt (computer scientist) (Wikipedia)
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In 1998, Platt invented sequential minimal optimization, a widely used algorithm for speeding up the training of support vector machines
View the Source David Haussler (Wikipedia)
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developments in computational learning theory and bioinformatics
View the Source Naftali Tishby (Wikipedia)
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He worked on the mathematical and statistical theory of learning and biological adaptation.
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