Browse Graduate Dissertations and Theses at Illinois by Subject "Machine Learning"

  • Stern, Raphael E (2015-04-27)
    In the aftermath of a natural disaster, knowledge of the connectivity of different regions of infrastructure networks is crucial to post-event decision making. The specific problem of determining the probability that two ...

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  • Jiang, Yiming (2016-04-26)
    Machine Learning (ML) is the science that enables computers with the ability to learn without being explicitly programmed. ML is so pervasive today, with applications in speech recognition, recommendation systems, fraud ...

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  • Aggarwal, Deepanshu (2014-01-16)
    This thesis describes a project on the modeling of spectral characteristics of electron density irregularities of the topside equatorial ionosphere probed by the Jicamarca Incoherent Scatter Radar (ISR) located near Lima, ...

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  • Goldwasser, Dan (2013-02-03)
    In this work we take a first step towards Learning from Natural Instructions (LNI), a framework for communicating human knowledge to computer systems using natural language. In this framework the process of learning is ...

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  • Juen, Joshua Paul Joseph (2015-08-18)
    Mobile devices contain sensors which allow continuous recording of a user's motion allowing the development of activity, fitness and health applications. With varied applications, the motion sensors present new privacy ...

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  • Connor, Michael (2012-02-06)
    A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign ...

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  • Hodosh, Micah A (2015-11-25)
    Automatically describing an image with a concise natural language description is an ambitious and emerging task bringing together the Natural Language and Computer Vision communities. With any emerging task, the necessary ...

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  • Guo, Ruiqi (2014-09-16)
    Humans can understand scenes with abundant detail: they see layouts, surfaces, the shape of objects among other details. By contrast, many machine-based scene analysis algorithms use simple representation to parse scenes, ...

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  • Chang, Kai-Wei (2015-04-23)
    The desired output in many machine learning tasks is a structured object, such as tree, clustering, or sequence. Learning accurate prediction models for such problems requires training on large amounts of data, making use ...

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  • Srikumar, Vivek (2013-05-24)
    The problem of ascribing a semantic representation to text is an important one that can help text understanding problems like textual entailment. In this thesis, we address the problem of assigning a shallow semantic ...

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  • Ji, Ming (2014-01-16)
    Real-world data entities are often connected by meaningful relationships, forming large-scale networks. With the rapid growth of social networks and online relational data, it is widely recognized that networked data are ...

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  • Cai, Deng (2009)
    Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (\ie, item-item similarity) ...

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  • Levine, Geoffrey C. (2012-02-06)
    In order for a machine learning effort to succeed, an appropriate model must be chosen. This is a difficult task in which one must balance flexibility, so that the model can capture the complexities of the domain, and ...

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  • Wen, Bihan (2015-10-30)
    In recent years, sparse signal modeling, especially using the synthesis dictionary model, has received much attention. Sparse coding in the synthesis model is, however, NP-hard. Various methods have been proposed to learn ...

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  • Kamalnath, Vishnu Nath (2013-08-22)
    This thesis deals with incorporating artificial intelligence into a humanoid robot by making a cognitive model of the learning process. The goal is to “teach” a specialized humanoid robot, the iCub robot, to solve any ...

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  • Wang, Li-Lun (2012-09-18)
    Statistical machine learning has achieved great success in many fields in the last few decades. However, there remain classification problems that computers still struggle to match human performance. Many such problems ...

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