Naive Bayes
The Naive Bayes classifier is based on the Bayes’ theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, the Naive Bayes classifier can often achieve comparable performance with some sophisticated classification methods, such as decision tree and selected neural network classifier. Naive Bayes classifiers have also exhibited high accuracy and speed when applied to large datasets. However, the assumption of independence between attributes makes accuracy less (since there is usually a linkage) 22.
SVM is a theoretically sound approach for controlling model complexity. It picks important instances to construct the separating surface between data instances. When the data is not linearly separable, it can either penalize violations with loss terms or leverage kernel tricks to construct non-linear separating surfaces. SVMs can also perform multiclass classifications in various ways, either by an ensemble of binary classifiers or by extending margin concepts. The optimization techniques of SVMs are mature, and SVMs have been used widely in many application domains. However, the SVM is difficult to use in large-scale problems. Large-scale in this case is meant by the number of samples being processed 22.