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Filtering by: Creator Reitz, Stuart R. Remove constraint Creator: Reitz, Stuart R. Committee Member Fern, Xiaoli Remove constraint Committee Member: Fern, Xiaoli Rights Statement In Copyright Remove constraint Rights Statement: In Copyright

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  • Near-contact Grasping Strategies: Improve Robotic Grasping from Awkward Poses with Deliberate Finger Motion

    Creator:
    Ong, Yi Herng
    Abstract:
    As robots are becoming more relevant to our lives, they are still having hard time accomplishing simple tasks such as picking and lifting. Problems that include environmental constraints, pose uncertainties and hardware noises restrain robots for grasping an object successfully from a perceivable environment. Many have looked into finding best...
    Resource Type:
    Masters Thesis
    Full Text:
    -2 0 2 4 6 x-axis (cm) -6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6 x-axis (cm) 0 0 .5 1 1 .5 R a d ia n

  • Rare category detection using hierarchical mean shift

    Creator:
    Vatturi, Pavan Kumar
    Abstract:
    Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
    Resource Type:
    Masters Thesis
    Full Text:
    the distance between this example and its Kthi4 nearest neighbor; Set r ′ i to be the minimum value

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
    Resource Type:
    Dissertation

  • Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

    Creator:
    Yao, Yao
    Abstract:
    We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
    Resource Type:
    Dissertation
    Full Text:
    define, for the sets L ⊂ Z+ of numbered loci, S ⊂ Z+ of numbered SNPs, and R ⊂ S of numbered rSNPs. Then

  • Interpretable Machine Learning: Applications in Biology and Genomics

    Creator:
    Danaee, Padideh
    Abstract:
    Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
    Resource Type:
    Dissertation

  • Exploration of regression models for cancer noncoding mutation recurrence

    Creator:
    Xu, Tanjin
    Abstract:
    An important impact of the genome technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Integrated with current well-studied massive knowledge and findings about the role of protein-coding mutations in cancer, demystifying the functional...
    Resource Type:
    Masters Thesis
    Full Text:
    → R. 2. Calculate the adjusted error eti for each instance: (a) let Dt = maxnj=1|yi − ht(xj)| (b

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
    Resource Type:
    Dissertation
    Full Text:
    , r t i) } , t = 1 . . . Li and s t i, a t i, and rti are the state, action and reward at time t