DiP-SVM: Distribution Preserving Kernel Support Vector Machine for Big Data

Dinesh Singh Debaditya Roy C. Krishna Mohan
Abstract: In literature, the task of learning a support vector machine for large datasets has been performed by splitting the dataset into manageable sized “partitions” and training a sequential support vector machine on each of these partitions separately to obtain local support vectors. However, this process invariably leads to the loss in classification accuracy as global support vectors may not have been chosen as local support vectors in their respective partitions. We hypothesize that retaining the original distribution of the dataset ...