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Filtering by: Alternative title ssim Technical bulletin 106 Remove constraint Alternative title ssim: Technical bulletin 106 Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Committee Member Raich, Raviv Remove constraint Committee Member: Raich, Raviv Rights Statement In Copyright Remove constraint Rights Statement: In Copyright

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  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
    Resource Type:
    Dissertation
    Full Text:
    . study. She has provided both excellent technical contributions and spiritual support to this disser

  • Anomaly Detection: Theory, Explanation and User Feedback

    Creator:
    Siddiqui, Md Amran
    Abstract:
    Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
    Resource Type:
    Dissertation
    Full Text:
    and Future Work . . . . . . . . . . . . . . . . . . . . . . . . 106 Bibliography

  • Learning ranking functions for efficient search

    Creator:
    Xu, Yuehua
    Abstract:
    This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . 106 8 Summary and Future Work. 111 8.1 Summary of Contributions

  • Visual object discovery and understanding

    Creator:
    Yuan, Jialin
    Abstract:
    Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
    Resource Type:
    Dissertation
    Full Text:
    including technical knowledge, communications, and teamwork. I am very grateful to my mentors and co