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  • A VHDL description of speech recognition front-end

    Creator:
    Xiao, Xin
    Abstract:
    This thesis investigates an implementation of speech recognition front-end. It is an application specific integrated circuit (ASIC) solution. A Mel Cepstrum algorithm is implemented for the feature extraction. We present a new mixed split-radix and radix-2 Fast Fourier Transform (FFT) algorithm, which can effectively minimize the number of complex multiplications...
    Resource Type:
    Masters Thesis
    Full Text:
    glad that Prof. Un-Ku Moon agreed to be my minor professor. I would also like to thank Prof. Jon

  • Designing and understanding information retrieval systems using collaborative filtering in an academic library environment

    Creator:
    Jung, Seikyung
    Abstract:
    Accessing information on the Web has become ingrained into our daily lives, and we seek information from many different sources, including conference and journal publications, personal web pages, and others. Increasingly, web-based information retrieval systems such as web-based search engines, library on-line catalog systems, and subscription-based federated search systems are...
    Resource Type:
    Dissertation
    Full Text:
    . Seikyung Jung, Author CONTRIBUTION OF AUTHORS Dr. Jon Herlocker was involved in the

  • Implementing a comparative anatomy information system for the foundational model of anatomy and the mouse anatomy ontology

    Creator:
    Diatchka, Kremena S.
    Abstract:
    This thesis describes the implementation of an interface for querying established correspondences between anatomical structures across species. I was the main developer of this query engine, called the Comparative Anatomy Information System. My work involved developing methods to query the knowledge base, perform the specified comparison, display the anatomical hierarchies...
    Resource Type:
    Honors College Thesis
    Full Text:
    and patiently answered questions to help me move on. From Oregon State University Jon Herlocker

  • Activity recognition in desktop environments

    Creator:
    Shen, Jianqiang
    Abstract:
    Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life. Intelligent desktop environments allow the desktop user to define a set of “activities” that characterize the user’s desktop work. These environments then attempt to identify...
    Resource Type:
    Dissertation
    Full Text:
    Fern, Jon Herlocker and Prasad Tadepalli for their excellent suggestions and thought-provoking

  • Applying machine learning for prediction, recommendation, and integration

    Creator:
    Bao, Xinlong
    Abstract:
    This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning. FolderPredictor is an intelligent desktop software tool that helps...
    Resource Type:
    Dissertation
    Full Text:
    Bergman, Alan Fern, Jon Herlocker and Prasad Tadepalli. They generously contributed their time and

  • Personalizing machine learning systems with explanatory debugging

    Creator:
    Kulesza, Todd
    Abstract:
    How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation...
    Resource Type:
    Dissertation
    Full Text:
    , as in the “white box” model proposed by Herlocker et al. (2000). To help people build mental models

  • On feature relevance feedback methods : incorporating labeled user features

    Creator:
    Oberst, Ian
    Abstract:
    In text classification, labeling features is often less time consuming than labeling entire documents. In situations where very little labeled training data is available, feature relevance feedback has the potential to dramatically increase classification performance. We review previous work on incorporating feature relevance feedback in the form of labeled features...
    Resource Type:
    Masters Thesis
    Full Text:
    thanks to Dr. Jon Herlocker for hiring me as an untested undergraduate research assistant. You opened

  • 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
    Full Text:
    , Professor Prasad Tadepalli, and Professor Jon Herlocker, for their kindly support and guidance to my PhD

  • Machine Learning Methods for Computational Sustainability

    Creator:
    Liu, Liping
    Abstract:
    Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
    Resource Type:
    Dissertation

  • 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:
    Tadepalli, Raviv Raich, and Jon Herlocker. They generously contributed their time and expertise to better