Kernel method in machine learning is defined as a class of pattern analysis algorithms that are used to study and find general types of relations (such as correlation, classification, ranking, clusters, principle components, and so on) in datasets by explicitly transforming raw data representation into feature vector representation using a user-specified feature map, allowing the high dimensional implicit feature space of these data to be operated with computers.