Browse by Subject "Machine Learning"

  • Endres, Ian (2013-08-22)
    Object recognition systems today see the world as a collection of object categories, each existing as a separate isolated entity. They exist in a closed world, never expecting to come across a new and unfamiliar object. ...

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  • Ratinov, Lev (2012-05-22)
    In recent decades, the society depends more and more on computers for a large number of tasks. The first steps in NLP applications involve identification of topics, entities, concepts, and relations in text. Traditionally, ...

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  • Chee, Brant (2012-02-06)
    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any ...

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  • Braz, Rodrigo de Salvo; Roth, Dan (2004-04)
    Most machine learning algorithms rely on examples represented propositionally as feature vectors. However, most data in real applications is structured and better described by sets of objects with attributes and relations ...

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  • Underwood, Ted (2013-09-16)
    This workset is data in support of the article "Mapping Mutable Genres in Structurally Complex Volumes," http://arxiv.org/abs/1309.3323. It is a .tsv file containing 32,209 lines, each of which corresponds to a volume in ...

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  • 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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  • 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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  • Hanneke, Steve; Roth, Dan (2004-06)
    We propose a unified perspective of a large family of semi-supervised learning algorithms, which select and label unlabeled data in an iterative process. We discuss existing approaches that label examples based on the ...

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  • Pu, Wen; Choi, Jaesik; Amir, Eyal; Espelage, Dorothy L. (2013-06-25)

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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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  • Vardhan, Abhay; Sen, Koushik; Viswanathan, Mahesh; Agha, Gul A. (2004-06)
    We present a novel approach for verifying safety properties of finite state machines communicating over unbounded FIFO channels that is based on applying machine learning techniques. We assume that we are given a model of ...

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  • Zelenko, Dmitry (2003-12)
    The dissertation presents a number of novel machine learning techniques and applies them to information extraction. The study addresses several information extraction subtasks: part of speech tagging, entity extraction, ...

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  • Bay, Mert (2013-02-03)
    Recently there has been a greater need to analyze, summarize, and categorize the increasing amount of audio content in the world. Most of this content comes from polyphonic music as mixtures of audio sources. Recently ...

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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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  • Wei, Qin; Heidorn, P. Bryan; Freeland, Chris (2010-02-03)
    Taxonomic Name Recognition is prerequisite for more advanced processing and mining of full-text taxonomic literatures. This paper investigates three issues of current TNR tools in detail: (1) The difficulties and methods ...

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  • Qian, Minglun (2005-04)
    In this thesis, we propose a recurrent FIR neural network, develop a constrained formulation for neural network learning, study an e_cient violation guided backpropagation algorithm for solving the constrained formulation ...

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  • Butz, Martin (2004-09)
    Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive ...

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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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