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Filtering by: Creator Lafferty, J. B., 1875- Remove constraint Creator: Lafferty, J. B., 1875- Advisor Dietterich, Thomas G. Remove constraint Advisor: Dietterich, Thomas G.

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  • Sequential supervised learning and conditional random fields

    Creator:
    Ashenfelter, Adam J.
    Abstract:
    Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all examples are independent from one another. The order of the examples contains no information. Nonetheless, many problems have a sequential nature. Classifiers for these problems...
    Resource Type:
    Masters Thesis
    Full Text:
    (i)Mt(j, i|wt)β̂t(j) 1.3.5 Performance of the CRF Lafferty, McCallum, and Pereira [10] compared the

  • 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:
    and b) more aggressive information extraction, such as finer descriptor encoding with large

  • Protein secondary structure prediction using conditional random fields and profiles

    Creator:
    Shen, Rongkun
    Abstract:
    Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
    Resource Type:
    Masters Thesis
    Full Text:
    j i j i j j j i a a j i j b b j i j i j j j i a b J P Y X y X y y X Z X y X y y X Z X g y X f y

  • Calibrating recurrent sliding window classifiers for sequential supervised learning

    Creator:
    Joshi, Saket Subhash
    Abstract:
    Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part-of-speech tagging and text-to-speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (RSW) classifier, which maps some window of the input sequence plus some number...
    Resource Type:
    Masters Thesis
    Full Text:
    given a set of N training examples, (Xi, Y)i1. 3 TABLE 1.1: The 8-Case Analysis 000 (a), (b), and

  • 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:
    as P (xt|yt) = ∏ j P (xt,j|yt) . This assumption of independence of each input feature xt,j

  • 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:
    all resources related to the current task; the Folder Predictor (b) provides 3 predicted folders in

  • 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
    Full Text:
    (SLL-B) . . . . . . 32 3.4.1 The general condition for learnability of SLL-B . . . . . . . . . . . . 33

  • Probabilistic models for quality control in environmental sensor networks

    Creator:
    Dereszynski, Ethan W.
    Abstract:
    Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
    Resource Type:
    Dissertation
    Full Text:
    environmental sensor. The baseline function b(qh, d) analyzed a historical record of observations from the