Comparing Accuracy of K-Nearest-Neighbor and Support-Vector-Machines for Age Estimation

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-38 Number-6
Year of Publication : 2016
Authors : Anchal Tomar, Anshika Nagpal
DOI :  10.14445/22315381/IJETT-V38P260


Anchal Tomar, Anshika Nagpal"Comparing Accuracy of K-Nearest-Neighbor and Support-Vector-Machines for Age Estimation", International Journal of Engineering Trends and Technology (IJETT), V38(6),326-329 August 2016. ISSN:2231-5381. published by seventh sense research group

In term for a robot machine or a computer to perform any task, it must recognize the problem and then act on it. Given a a picture a computer must have the capacity to group/classify what the picture represents exactly. While this is a genuinely simple for humans, it is not an ethereal task for computers. Computers must experience a progression of steps in order to classify a single picture. So, there are different techniques, tools and procedures are introduced. And here explained two algorithms: (SVM) Support Vector Machine and K Nearest Neighbor (KNN) classification. Support vector machine is a model for measurements and software engineering which perform supervised learning, methods that are used for analysing data and recognize various patterns. SVM is used for classification and regression analysis. Likely knearest neighbor algorithm is also a classification algorithm but it is used to classify data using training examples. In this paper SVM and KNN algorithm are explained and also evaluate which one has good accuracy and in which conditions.


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Support Vector-Machine (SVM), supervised learning, regression analysis, K-Nearest Neighbor (KNN), classification.