000			03141nam a22001937a 4500
003			OSt
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040			_cIIITMK
100			_aAfzal A L _916890
245			_aDeep learning in kernel methods _cAfzal A L
300			_aPHD Thesis 2018
500			_aThe attempt to build algorithms to solve cognitive tasks such as visual object or pattern recognition, speech perception, language understanding etc. have attracted the attention of many machine learning researchers in the recent past. The theoretical and biological arguments in this context strongly suggest that building such systems requires deep learning architectures that involve many layers of nonlinear information processing. Deep learning approach has originally emerged and been widely used in the area of neural networks. The techniques developed from deep learning research have already been impacting a wide range of signal and information processing applications. In the recent past, excited by the startling performance that deep learning approaches have to offer, there are many attempts to embrace deep learning techniques in other machine learning paradigms, particularly in kernel machines. Convex optimization, structural risk minimization, margin maximization, etc. are the some of the elegant features that makes kernel machines popular among the researchers. With the advent of recently developed multi layered kernel called arc-cosine kernel, the multilayer computations is made possible in kernel machines. The multi-layered feature learning perceptiveness of deep learning architecture have been re-created in kernel machines through the model called Multilayer Kernel Machines(MKMs). Support vector machines were often used as the classifier in these models. These deep models have been widely used in many applications that involves small-size datasets. However the scalability, multilayer multiple kernel learning, unsupervised feature learning etc. were untouched in the context of kernel machines. This research explored above problems and developed three deep kernel learning models viz; (i) Deep kernel learning in core vector machine that analyze the behavior of arc-cosine kernel and modeled a scalable deep kernel machine by incorporating arc-cosine kernel in core vector machines. (ii) Deep multiple multilayer kernel learning in core vector machines modeled a scalable deep learning architecture with unsupervised feature extraction. Each feature extraction layer in this model exploit multiple kernel learning framework that involves both single layer and multilayer kernel computations. (iii) Deep kernel based extreme learning machine combines the multilayer kernel computation of arc-cosine kernel and fast, non-iterative learning mechanism of Extreme Learning Machines. The theoretical and empirical analysis of the proposed methods show promising results.
502			_bPhD Thesis _cSeptember 2018 _dINT _eDr Asharaf S IIITMK
650			_aDEEP LEARNING _916891
650			_aKERNEL MACHINES _916892
650			_aMULTILAYER KERNEL MACHINES _916893
942			_2ddc _cTS
999			_c6629 _d6629