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Filtering by: Creator Strahl, Jordan D. Remove constraint Creator: Strahl, Jordan D. Committee Member Wong, Weng-Keen Remove constraint Committee Member: Wong, Weng-Keen

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  • A probabilistic framework and algorithms for modeling and analyzing multi-instance data

    Creator:
    Behmardi, Behrouz
    Abstract:
    Multi-instance data, in which each object (e.g., a document) is a collection of instances (e.g., word), are widespread in machine learning, signal processing, computer vision, bioinformatic, music, and social sciences. Existing probabilistic models, e.g., latent Dirichlet allocation (LDA), probabilistic latent semantic indexing (pLSI), and discrete component analysis (DCA), have been...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.8.1 Derivative of λ12 ∥D 1λ1 (Ψ′)∥2F with respect to λ1 . . . . . . . . . . . 54 2.8.2 Proof

  • Active learning of constraints for semi-supervised clustering

    Creator:
    Xiong, Sicheng
    Abstract:
    Semi-supervised clustering aims to improve clustering performance by considering user supervision in the form of pairwise constraints. In this paper, we study the active learning problem of selecting pairwise must-link and cannot-link constraints for semisupervised clustering. We consider active learning in an iterative manner where in each iteration queries are...
    Resource Type:
    Masters Thesis
    Full Text:
    . . . . . . . . . . . . . . . . . . 12 2 MostInformative(D,π, N ) . . . . . . . . . . . . . . . . . . . . . . . . 18 To my family and

  • A study of methods for fine-grained object classification of arthropod specimens

    Creator:
    Lin, Junyuan
    Abstract:
    Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
    Resource Type:
    Masters Thesis
    Full Text:
    images; d is the dimensionality of the feature vectors, T is the depth of the decision trees, and k is

  • 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:
    incorrect modeling assumptions (Ng and Jordan, 2002). McCallum and his collaborators introduced maximum

  • Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

    Creator:
    Ibrahim, Shahana
    Abstract:
    Crowdsourcing is a popular paradigm to address the high demands for labeled data in big data deluge. It aims to produce accurate labels by effectively integrating noisy, non-expert labeling from crowdsourced workers (annotators). The machine learning community has been studying effective crowdsourcing methods for many years, and many models and...
    Resource Type:
    Masters Thesis
    Full Text:
    (%) & Averge run-time when d = [13 , 1 3 , 1 3 ] > . . . . . . 33 5.4 Classification Error(%) & Averge run

  • Integrating learning and search for structured prediction

    Creator:
    Doppa, Janardhan Rao
    Abstract:
    We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
    Resource Type:
    Dissertation
    Full Text:
    the expected target depth of our Flipbit-null space (E[d]). . . . . . . . . . . . . . . . . . . . 89

  • Creating, Understanding and Applying Machine Learning Models of Multiple Species

    Creator:
    Griffioen, Arwen Twinkle E.
    Abstract:
    Many problems in ecology and conservation biology can be formulated and solved using machine learning algorithms for multi-label classification. This dissertation addresses three topics related to predicting the distributions of multiple species. It improves existing methods and proposes a new modeling paradigm to address the multi-species, multi-label problem. The first...
    Resource Type:
    Dissertation
    Full Text:
    under Contract No. FA8750-07-D-0185/0004 through the Air Force Research Laboratory (AFRL). Any

  • Effects of spatial scale and heterogeneity on avian occupancy dynamics and population trends in forested mountain landscapes

    Creator:
    Frey, Sarah J. K.
    Abstract:
    Population trends and patterns in species distributions are the major currencies used to examine responses by biodiversity to changing environments. Effective conservation recommendations require that models of both distribution dynamics and population trends accurately reflect reality. However, identification of the appropriate temporal and spatial scales of animal response, and then...
    Resource Type:
    Dissertation
    Full Text:
    exposed areas also are more variable in temperature during this period (SDwTJM 2013). (D) Aspect played a

  • Effective decision-theoretic assistance through relational hierarchical models

    Creator:
    Natarajan, Sriraam
    Abstract:
    Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the...
    Resource Type:
    Dissertation

  • Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

    Creator:
    Briggs, Forrest
    Abstract:
    We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their...
    Resource Type:
    Dissertation
    Full Text:
    mask of the highlighted segment. (d) The masked and cropped spectrogram corresponding to the