Abstract
A support vector machine (SVM) is a classification technique in the field of data mining, used for the classification of both linear as well as non-linear data. It learns the decision surface from two different classes of input samples and then performs analysis of new input samples. A neural network is able to learn without the explicit description of the problem or the need of a programmer. Another type of classification technique is the decision tree. In this paper, we are doing a comparative study of the above mentioned classification techniques by analyzing their performance on data sets. We will be comparing the inputs and the observed outputs.
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Raychaudhuri, K., Kumar, M., Bhanu, S. (2017). A Comparative Study and Performance Analysis of Classification Techniques: Support Vector Machine, Neural Networks and Decision Trees. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_2
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DOI: https://doi.org/10.1007/978-981-10-5427-3_2
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