S. Asharaf

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Support vector machines (SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper, we propose a novel kernel based incremental data clustering approach and its use for scaling(More)
Documents which are published both online and offline are considered to be the primary source of information. Astonishing growth of documentation and communication systems tends to flood these pools of information sources with enormous amount of documents. In such a scenario, it is critical to envisage algorithms and methodologies that can convert these(More)
In machine learning literature, deep learning methods have been moving toward greater heights by giving due importance in both data representation and classification methods. The recently developed multilayered arc-cosine kernel leverages the possibilities of extending deep learning features into the kernel machines. Even though this kernel has been widely(More)