Performance Analysis of Support Vector Machine Algorithms
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Abstract
The analysis of computer algorithms which automatically develop through experience is machine learning. An
algorithm for supervised learning learns from classied training data and lets users forecast results for unforeseen data. The aim of this research is to analyze the three machine learning algorithms such as support vector machine, linear support vector machine and an innovative version termed as L1 Panelized SVM on three dierent data sets. Applying the 5 folds cross-validation, the results of various performance metrics determine the comparison based on the three data sets. This research study can be extended for developing models of distributed machine learning algorithms for two cases of machine learning: gradient descent and graphical model reference, along with ensemble algorithms like AdaBoost and XGBoost.
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