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A conjectural study on machine learning algorithms

Publication Type : Journal Article

Publisher : Advances in Intelligent Systems and Computing

Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 397, p.105-116 (2016)

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ISBN : 9788132226697

Keywords : Adaptive boosting, Agglomerative clustering, Algorithms, Artificial intelligence, Bagging, Boosting, Clustering algorithms, Decision trees, Divisive clustering, Hier-archical clustering, Intelligent systems, K-means clustering, Learning algorithms, Learning systems, Logistic regressions, Partitional clustering, Problem solving, Random forests, Soft computing, Supervised learning, Unsupervised learning

Campus : Coimbatore

School : School of Engineering

Center : Electronics Communication and Instrumentation Forum (ECIF)

Department : Computer Science, Communication

Year : 2016

Abstract : Artificial Intelligence, a field which deals with the study and design of systems, which has the capability of observing its environment and does functionalities which aims at maximizing the probability of its success in solving problems. AI turned out to be a field which captured wide interest and attention from the scientific world, so that it gained extraordinary growth. This in turn resulted in the increased focus on a field—which deals with developing the underlying conjectures of learning aspects and learning machines—machine learning. The methodologies and objectives of machine learning played a vital role in the considerable progress gained by AI. Machine learning aims at improving the learning capabilities of intelligent systems. This survey is aimed at providing a theoretical insight into the major algorithms that are used in machine learning and the basic methodology followed in them. © Springer India 2016.

Cite this Research Publication : A. Sankar, Bharathi, P. D., Midhun, M., Vijay, K., and Dr. Senthil Kumar T., “A conjectural study on machine learning algorithms”, Advances in Intelligent Systems and Computing, vol. 397, pp. 105-116, 2016.

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