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In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems.
An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating the weights and bias levels of a network when a network is simulated in a specific data environment. A learning rule may accept existing conditions of the network and will compare the expected result and actual result of the network to give new and improved values for weights and bias. Depending on the complexity of actual model being simulated, the learning rule of the network can be as simple as an XOR gate or mean squared error, or as complex as the result of a system of differential equations.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms.
The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
Plamen P. Angelov is a computer scientist. He is a professor and chair in Intelligent Systems and Director of Research at the School of Computing and Communications of Lancaster University, Lancaster, United Kingdom. He is founding Director of the Lancaster Intelligent, Robotic and Autonomous systems (LIRA) research centre. Angelov was Vice President of the International Neural Networks Society of which he is now Governor-at-large. He is the founder of the Intelligent Systems Research group and the Data Science group at the School of Computing and Communications. Prof. Angelov was named Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2016 for contributions to neuro-fuzzy and autonomous learning systems. He is also a Fellow of ELLIS and the IET. Dr. Angelov is a founding co-Editor-in-chief of the Evolving Systems journal since 2009 as well as associate editor of the IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, IEEE Transactions on AI, Complex and Intelligent Systems and other scientific journals. He is recipient of the 2020 Dennis Gabor Award as well as IEEE and INNS awards for Outstanding Contributions, The Engineer 2008 special award and others. Author of over 350 publications including 3 research monographs, 3 granted US patents, over 100 articles in peer reviewed scientific journals, over 150 papers in peer reviewed conference proceedings, etc.
Charles Chapman Pugh is an American mathematician who researches dynamical systems. Pugh received his PhD under Philip Hartman of Johns Hopkins University in 1965, with the dissertation The Closing Lemma for Dimensions Two and Three. He has since been a professor, now emeritus, at the University of California, Berkeley.
The history of artificial neural networks (ANN) began with Warren McCulloch and Walter Pitts (1943) who created a computational model for neural networks based on algorithms called threshold logic. This model paved the way for research to split into two approaches. One approach focused on biological processes while the other focused on the application of neural networks to artificial intelligence. This work led to work on nerve networks and their link to finite automata.
References
- ↑ Lemaire, Vincent; Salperwyck, Christophe; Bondu, Alexis (2015). "A Survey on Supervised Classification on Data Streams". Business Intelligence. Lecture Notes in Business Information Processing. Vol. 205. pp. 88–125. doi:10.1007/978-3-319-17551-5_4. ISBN 978-3-319-17550-8. S2CID 26990770.
- ↑ Angelov, Plamen (2008). "Evolving fuzzy systems". Scholarpedia. 3 (2): 6274. Bibcode:2008SchpJ...3.6274A. doi: 10.4249/scholarpedia.6274 .
- ↑ Angelov, Plamen (2008). "Evolving fuzzy systems". Scholarpedia. 3 (2): 6274. Bibcode:2008SchpJ...3.6274A. doi: 10.4249/scholarpedia.6274 .
- ↑ Lughofer, E.; Buchtala, O. (2013). "Reliable All-Pairs Evolving Fuzzy Classifiers". IEEE Transactions on Fuzzy Systems. 21 (4): 625–641. doi:10.1109/TFUZZ.2012.2226892. S2CID 29586197.
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