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    Browsing by Advisor "Sastry, P S"

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    Now showing items 1-13 of 13

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      • Design and Analysis of Real-time Message Scheduling under FlexRay Protocol 

        Sunil Kumar, P R (2018-05-29)
        A typical automobile system consists of many Electronic Control Units (ECUs) for the purposes of safety, comfort, and entertainment applications. FlexRay is a high bandwidth protocol for such automotive requirements, which ...
      • Discovering Frequent Episodes : Fast Algorithms, Connections With HMMs And Generalizations 

        Laxman, Srivatsan (2008-10-13)
        Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come ...
      • Effective Characterization of Sequence Data through Frequent Episodes 

        Ibrahim, A (2018-08-14)
        Pattern discovery is an important area of data mining referring to a class of techniques designed for the extraction of interesting patterns from the data. A pattern is some kind of a local structure that captures correlations ...
      • Efficient Algorithms for Learning Restricted Boltzmann Machines 

        Upadhya, Vidyadhar
        The probabilistic generative models learn useful features from unlabeled data which can be used for subsequent problem-specific tasks, such as classification, regression or information retrieval. The RBM is one such important ...
      • Fatty Acid And Triacylglycerol Synthesis In Developing Seeds Of Groundnut (Arachis Hypogaea) And Pisa (Actinodaphne Hookeri) 

        Sreenivas, Avula (Indian Institute of Science, 2005-04-18)
        The term "lipid" covers an extremely diverse range of chemical or molecular species. Lipids, defined as molecules that are sparingly soluble in water but readily soluble in organic solvents, are broadly categorized into ...
      • Frequent Episode Mining: Efficient Discovery Algorithms and Significance Analysis 

        Gandreti, Santhosh B
        Frequent Pattern mining is a popular area of data mining, where the goal is to unearth interesting patterns that occur often in a given data. A pattern is a local structure that captures correlations and dependencies present ...
      • Hyperplane Clustering : A New Divisive Clustering Algorithm 

        Yogananda, A P (2011-05-05)
      • Multisource Subnetwork Level Transfer in Deep CNNs Using Bank of Weight Filters 

        Kirthi, Suresh K
        The convolutional neural networks (CNNs) have become the most successful models for many pattern recognition problems in the areas of computer vision, speech, text and others. One concern about CNNs has always been their ...
      • Oxidative Stress In The Brain: Effects Of Hydroperoxides And Nitric Oxide On Glyceraldehyde 3-Phosphate Dehydrogenase And Phosphoinositide Cycle Enzymes 

        Vaidyanathan, V V (Indian Institute of Science, 2005-09-01)
        In the aerobic cell, oxygen can be converted into a series of reactive metabolites, together called as "reactive oxygen species" (ROS). This large group include both radical and non-radical species such as superoxide anion ...
      • Robust Distribution-Free Learning Of Logic Expressions 

        Rajaraman, K (2012-05-24)
      • Robust Risk Minimization under Label Noise 

        Kumar, Himanshu
        In the setting of supervised learning, one learns a classi fier from training data consisting of patterns and the corresponding labels. When labels of the examples in training data have errors, it is referred to as label ...
      • A Study Of Utility Of Smile Profile For Face Recognition 

        Bhat, Srikrishna K K (2008-09-09)
        Face recognition is one of the most natural activities performed by the human beings. It has wide range of applications in the areas of Human Computer Interaction, Surveillance, Security etc. Face information of people can ...
      • Supervised Learning of Piecewise Linear Models 

        Manwani, Naresh (2018-03-07)
        Supervised learning of piecewise linear models is a well studied problem in machine learning community. The key idea in piecewise linear modeling is to properly partition the input space and learn a linear model for every ...

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