Browse Dept. of Computer Science by Subject "Machine Learning"

  • Samdani, Rajhans (2014-01-16)
    Structured prediction describes problems which involve predicting multiple output variables with expressive and complex interdependencies and constraints. Learning over expressive structures (called structural learning) ...

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  • Pekcan, Onur (2010-05-19)
    In this thesis, we describe a biometric authentication system that is capable of recognizing its users’ voice using advanced machine learning and digital signal processing tools. The proposed system can both validate a ...

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  • Lu, Xun (2014-05-30)
    Medical specialties provide essential information about which providers have the skills needed to carry out key procedures or make critical judgments. They are useful for training and staffing and provide confidence to ...

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  • 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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  • 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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  • 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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  • 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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  • 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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