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Filtering by: Creator Edwards, Daniel K. Remove constraint Creator: Edwards, Daniel K. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Shrunken learning rates do not improve AdaBoost on benchmark datasets

    Creator:
    Forrest, Daniel L. K.
    Abstract:
    Recent work has shown that AdaBoost can be viewed as an algorithm that maximizes the margin on the training data via functional gradient descent. Under this interpretation, the weight computed by AdaBoost, for each hypothesis generated, can be viewed as a step size parameter in a gradient descent search. Friedman...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Daniel L. K. Forrest for the

  • Nanoparticle-assisted diffusion brazing of metal microchannel arrays : nanoparticle synthesis, deposition, and characterization

    Creator:
    Eluri, Ravindranadh T.
    Abstract:
    Microchannel process technology (MPT) offers several advantages to the field of nanomanufacturing: 1) improved process control over very short time intervals owing to shorter diffusional distances; and 2) reduced reactor size due to high surface area to volume ratios and enhanced heat and mass transfer. The objective of this thesis...
    Resource Type:
    Dissertation
    Full Text:
    . Tiwari, Gopi K. Lingam, Clayton Hires, Barath Palanisamy, Daniel Peterson and Kathy Han for their

  • Towards reliable and survivable ocean wave energy converters

    Creator:
    Brown, Adam C.
    Abstract:
    Ocean wave energy is a new and developing field of renewable energy with great potential. The energy contained in one meter of an average wave off the coast of Newport Oregon could supply dozens of homes with electricity. However, ocean waves are usually quite irregular which leads to large bursts...
    Resource Type:
    Masters Thesis
    Full Text:
    to thank my brother Daniel, who has shared my time at Oregon State. Your advice on a wide range of

  • Bayesian optimization with empirical constraints

    Creator:
    Azimi, Javad
    Abstract:
    Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
    Resource Type:
    Dissertation
    Full Text:
    select k ≥ 1 points to be evaluated. The results of those points are then added to the set of

  • Resource placement, data rearrangement, and Hamiltonian cycles in torus networks

    Creator:
    Bae, Myung Mun
    Abstract:
    Many parallel machines, both commercial and experimental, have been/are being designed with toroidal interconnection networks. For a given number of nodes, the torus has a relatively larger diameter, but better cost/performance tradeoffs, such as higher channel bandwidth, and lower node degree, when compared to the hypercube. Thus, the torus is...
    Resource Type:
    Dissertation
    Full Text:
    . The last part of the thesis gives results on generating edge disjoint Hamilto­ nian cycles in k-ary n

  • D₊₊ : Structural Credit Assignment in Tightly Coupled Multiagent Domains

    Creator:
    Rahmattalabi, Aida
    Abstract:
    Autonomous multiagent teams can be used in complex exploration tasks to both expedite the exploration and improve the efficiency. However, use of multiagent systems presents additional challenges. Specifically, in domains where the agents' actions are tightly coupled, coordinating multiple agents to achieve cooperative behavior at the group level is difficult....
    Resource Type:
    Masters Thesis
    Full Text:
    extracted for each agent to form a team which then operates in the problem domain. At each generation, k

  • Topics in toroidal interconnection networks

    Creator:
    Broeg, Robert Richard
    Resource Type:
    Dissertation
    Full Text:
    characterized as a graph that is the cross product of cycles. The graphs of the k-ary n-cube (Qk,,) and the

  • 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
    Full Text:
    couple of years. Special thanks to Dr. Christopher K. Mathews and Dr. Andy Karplus for their remarkable

  • Scheduling and Online Planning in Stochastic Diffusion Networks

    Creator:
    Xue, Shan
    Abstract:
    Diffusion processes in networks are common models for many domains, including species colonization, information/idea cascade, disease propagation and fire spreading. In diffusion networks, a diffusion event occurs when a behavior spreads from one node to the other following a probabilistic model, where the behavior could be species, an idea, a...
    Resource Type:
    Dissertation
    Full Text:
    was brought in by Daniel Sheldon when he worked as a postdoc of Prof. Dietterich. His knowledge of

  • A SAT-based planning framework for optimizing resource production

    Creator:
    Ventura, Christopher
    Abstract:
    Domain-independent automated planning is concerned with computing a sequence of actions that can transform an initial state into a desired goal state. Resource production domains form an interesting class of such problems, in that they typically require reasoning about concurrent durative-actions with continuous effects while minimizing some cost function. Although...
    Resource Type:
    Masters Thesis
    Full Text:
    Stephen Wolfman and Daniel Weld (see [23], [21], [22]), that combines a satisfiability (SAT) solver with