A Comparative Study of Training Algorithms for Supervised Machine Learning
Hetal Bhavsar1, Amit Ganatra2

1Hetal Bhavsar, Information Technology Dept., SVIT, Vasad, Gujarat, India.
2Amit Ganatra Computer Engineering Dept., CHARUSAT, Changa, Gujarat, India.
Manuscript received on September 01, 2012. | Revised Manuscript received on September 02, 2012. | Manuscript published on September 05, 2012. | PP: 74-81 | Volume-2 Issue-4, September 2012. | Retrieval Number: D0887072412/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Classification in data mining has gained a lot of importance in literature and it has a great deal of application areas from medicine to astronomy, from banking to text classification.. It can be described as supervised learning algorithm as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class label. The classification techniques are help to learn a model from a set of training data and to classify a test data well into one of the classes. This research is related to the study of the existing classification algorithm and their comparative in terms of speed, accuracy, scalability and other issues which in turn would help other researchers in studying the existing algorithms as well as developing innovative algorithms for applications or requirements which are not available.
Keywords: Classification, decision tree, nearest neighbour, neural network, SVM, Supervised learning