A survey of machine learning techniques Online publication date: Tue, 09-Jan-2018
by I. Devi; G.R. Karpagam; B. Vinoth Kumar
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 3, No. 4, 2017
Abstract: Artificial intelligence (AI) allows the systems to observe from environments, perform certain functionalities and aims to maximise the probability of success in solving real world problems. With the technological enhancements and scientific growth, AI turns out to be a field of interest. Thus, it leads to the amplified focus on machine learning (ML) techniques. Machine learning (ML) is the most important data analysis methods which iteratively learn from the available data by using learning algorithms. The present survey provides the theoretical representation and basic methodologies of machine learning techniques like support vector machine (SVM), K-nearest neighbours (KNNs), decision tree, Bayesian networks, clustering, hidden Markov model (HMM) and neural networks. This survey paper provides the influence of machine learning techniques like clustering, SVM and ANN on image compression and attention to the existing scope for the image compression with machine learning.
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