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Ensamble Learning: An Approach in Artificial Intelligence
Author(s) -
Swapnali G. Kadao,
Rupali B. Surve
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-v2-i3-327
Subject(s) - computer science , artificial intelligence , machine learning , field (mathematics) , face (sociological concept) , unsupervised learning , data science , social science , mathematics , sociology , pure mathematics
Decades ago, in the field of machine learning and data mining, the development of methods of ensemble learning has received significant attention from science community. Machine integration techniques incorporate multiple learning acquisition skills, better performance of guesswork than you would find in any available learning skills alone. Combining multiple learning models is demonstrated in thought and experimentation providing better performance than single foundation students. In a book, mix learning algorithms form a dominant and high-level approach to high throughput performance, thus applied to real-world problems ranging from face to face emotional recognition through classification and medical diagnosis in financial forecasting.

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