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  • Machine Learning for Seizure Prediction:A Revamped Approach

    K. V. Sreerama Murthy, Deepthi Karnam, saikumar allaka, Lavi Nigam
    International Conference on Advances in Computing, Communications & Informatics

    Occurrence of multiple seizures is a common phenomenon observed in patients[...]

  • Bug Prediction Metrics Based Decision Support For Preventive Software Maintenance

    Deepthi Karnam, Sree Aurovindh Viswanathan, Girish Maskeri Rama
    APSEC (Asia Pacific Software Engineering Conference)

    There exist a number of large legacy systems that still undergo continuous maintenance and[...]

  • Version History Based Source Code Plagiarism Detection in Proprietary Systems

    Deepthi Karnam, Sree Aurovindh Viswanathan, Girish Maskeri Rama
    Internation Conference on Software Maintenance

    While the advent of open source code search tools have made the source code of thousands of[...]

  • Self-Tuning Energy-Aware Ensemble Model for Server Clusters

    Deepthi Karnam, Sanket Dangi, Sudha M, Celina Madhavan, Shrisha Rao
    Global Science & Technology Forum (GSTF) Singapore

    Server Clusters have become integral components to operate Internet Services[...]

  • Automatic Collimation in Peripheral X-ray Imaging

    S Murthy, J Qian

    A method has been developed using which the foreground (body) in a fluoroscopy image of the peripherals can be segmented from[...]

  • Investigations of the greedy heuristic for classification tree induction

    SK Murthy, SL Salsberg

    Most existing methods for automatic construction of classi cation trees utilize the greedy heuristic[...]

  • Automatic construction of decision trees from data: A multi-disciplinary survey

    SK Murthy,
    Data mining and knowledge discovery 2 (4), 345-389

    Decision trees have proved to be valuable tools for the description, classification and generalization[...]

  • Statistical Preprocessing for Decision Tree Induction

    SK Murthy
    Technical Report, John Hopkins University, Baltimore, Maryland, USA.

    Some apparently simple numeric data sets cause signi cant problems for existing decision tree induction algorithms,[...]

  • Discovering morphemic su xes: A case study in mdl induction

    M Brent, SK Murthy, A Lunsberg
    Fifth International Workshop on AI and Statistics, Ft. Lauderdale, florida

    Abstract This paper reports experiments in the automatic discovery of linguistically sign[...]

  • Lookahead and Pathology

    S Murthy
    in Decision Tree Induction", IJCAI-95

    The standard approach t decision tree in duction is a top-down greedy agonthm that makes locall} optimal irrevocable decisions at each node of a tree[...]

  • Decision Tree Induction: How Effective Is the Greedy Heuristic?

    SK Murthy, S Salzberg
    KDD, 222-227

    Most existing decision tree systems use a greedy approach to induce trees--locally optimal splits are[...]

  • Discovering morphemic suffixes: A case study in minimum description length induction

    MR Brent, SK Murthy, A Lundberg

    Proceedings of the fifth international workshop on artificial intelligence and statistics[...]

  • Decision trees for automated identification of cosmic-ray hits in Hubble Space Telescope images

    S Salzberg, R Chandar, H Ford, SK Murthy, R White
    Publications of the Astronomical Society of the Pacific, 279-288

    We have developed several algorithms for classifying objects[...]

  • Lookahead and pathology in decision tree induction

    S Murthy, S Salzberg
    IJCAI, 1025-1033

    The standard approach to decision tree induction is a top-down, greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree.[...]