Browse by Subject "Machine Learning"

  • 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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  • 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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  • 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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  • 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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