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Review on Various Machine Learning and Deep Learning Techniques for Prediction and Classification of Quotidian Datasets
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Author(s): Anisha M. Lal (Vellore Institute of Technology, India), B. Koushik Reddy (Vellore Institute of Technology, India)and Aju D. (Vellore Institute of Technology, India)
Copyright: 2020
Pages: 28
Source title:
Recent Advances in 3D Imaging, Modeling, and Reconstruction
Source Author(s)/Editor(s): Athanasios Voulodimos (University of West Attica, Athens, Greece)and Anastasios Doulamis (National Technical University of Athens, Athens, Greece)
DOI: 10.4018/978-1-5225-5294-9.ch014
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Abstract
Machine learning can be defined as the ability of a computer to learn and solve a problem without being explicitly coded. The efficiency of the program increases with experience through the task specified. In traditional programming, the program and the input are specified to get the output, but in the case of machine learning, the targets and predictors are provided to the algorithm make the process trained. This chapter focuses on various machine learning techniques and their performance with commonly used datasets. A supervised learning algorithm consists of a target variable that is to be predicted from a given set of predictors. Using these established targets is a function that plots targets to a given set of predictors. The training process allows the system to train the unknown data and continues until the model achieves a desired level of accuracy on the training data. The supervised methods can be usually categorized as classification and regression. This chapter discourses some of the popular supervised machine learning algorithms and their performances using quotidian datasets. This chapter also discusses some of the non-linear regression techniques and some insights on deep learning with respect to object recognition.
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