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  • Personalizing machine learning systems with explanatory debugging

    Creator:
    Kulesza, Todd
    Abstract:
    How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation...
    Resource Type:
    Dissertation
    Full Text:
    Debugging AN ABSTRACT OF THE DISSERTATION OF Todd Kulesza for the degree of Doctor of Philosophy in

  • Toward end-user debugging of machine-learned programs

    Creator:
    Kulesza, Todd
    Abstract:
    The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters because without the ability to fix errors, users may find that the learned program’s errors...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Todd Kulesza for the degree of Master of Science in Computer Science

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
    Resource Type:
    Dissertation
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    Modes for Sequential Decision Making Abstract approved: Alan P. Fern This thesis considers the

  • Object detection in biological images using a search-based framework

    Creator:
    Lam, Michael
    Abstract:
    This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to detect nematocysts in Scanning Electron Microscope (SEM) images. This biological domain presents challenges for existing...
    Resource Type:
    Masters Thesis
    Full Text:
    Framework AN ABSTRACT OF THE THESIS OF Michael Lam for the degree of Master of Science in Computer

  • Learning classification rules by randomized iterative local search

    Creator:
    Chisholm, Michael (Michael Andrew), 1974-
    Abstract:
    Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning algorithms for this problem employ some variation of a greedy separate-and-conquer algorithm. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules from examples using a...
    Resource Type:
    Masters Thesis
    Full Text:
    Microsoft Word - MS Thesis 6.doc AN ABSTRACT OF THE THESIS OF Michael

  • Investigating Transcriptional Control of Specialized Gene Expression in Plants

    Creator:
    Fraser, Valerie N.
    Abstract:
    Specialized or secondary metabolism is a collection of pathways and small molecules that, while beneficial to an organism, are not strictly necessary for survival. Plants use secondary metabolites to, among other things, attract pollinators, defend against biotic and abiotic stressors, and form symbioses. Natural products from plants have seen an...
    Resource Type:
    Dissertation
    Full Text:
    retired. To each of my committee members—Dr. John Fowler, Dr. Jeffrey Anderson, Dr. Michael Freitag

  • Machine learning in engineering : techniques to speed up numerical optimization

    Creator:
    Cerbone, G. (Giuseppe)
    Abstract:
    Many important application problems in engineering can be formalized as nonlinear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. For example, these methods depend critically on choosing a good starting point from which to perform the optimization search. In high-dimensional spaces, numerical...
    Resource Type:
    Dissertation
    Full Text:
    Tadepalli provided useful feedback on EBL related issues and proof-read most of my papers. Michael Scanlan

  • Towards a national map of soil liquefaction susceptibility : modeling with heuristic and geotechnical methods

    Creator:
    Lindsley, Adam E.
    Abstract:
    This study investigates the use of predictive mapping techniques as well as geotechnical criteria in developing a multiregional soil liquefaction model and subsequent maps. The maps were produced using National Cooperative Soil Survey data, in the gSSURGO format, combined with soil liquefaction data gathered from studies, articles, and traditional seismic...
    Resource Type:
    Masters Thesis
    Full Text:
    challenging project. Thank you also to my committee members, Maria Dragila, John Baham, and Michael Olsen

  • Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale

    Creator:
    Hooper, Samuel
    Abstract:
    Developing accurate predictive distribution models requires adequately representing relevant spatial and temporal scales, as these scales are ultimately reflective of the relationships between distributions and influential environmental conditions. In this research, we considered both spatial and temporal scale and the influence each has on predicting broad-scale distributions of two disparate...
    Resource Type:
    Masters Thesis
    Full Text:
    . References Addicott, John F., John M. Aho, Michael F. Antolin, Dianna K. Padilla, John S. Richardson, and

  • Robust and Efficient Classification of Videos in the Wild

    Creator:
    Mahasseni, Behrooz
    Abstract:
    Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
    Resource Type:
    Dissertation
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    , Anirban Roy, Michael Lam, Sheng Chen, Peng Lei, Christos Margiolas, Alberto Garcia, Suren Jayasuriya and

  • Integrating learning and search for structured prediction

    Creator:
    Doppa, Janardhan Rao
    Abstract:
    We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
    Resource Type:
    Dissertation
    Full Text:
    : Integrating Learning and Search for Structured Prediction Abstract approved: Prasad Tadepalli Alan P. Fern

  • 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
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    observed label set Yn is drawn from a set-valued distribution p(Yn|yn) that depends only on the true label

  • Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

    Creator:
    Briggs, Forrest
    Abstract:
    We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their...
    Resource Type:
    Dissertation

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation
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    thanks to Michael Slater, who is not only the primary devel- oper of the software (TAPE) used in this

  • 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

  • Learning greedy policies for the easy-first framework

    Creator:
    Xie, Jun
    Abstract:
    Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
    Resource Type:
    Masters Thesis
    Full Text:
    average of MUC, B3, and CEAF-φ4 scores [30]. 16 MUC B3 CEAF-φ4 Approach R P F1 R P F1 R P F1 CoNLL F1

  • Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

    Creator:
    Lei, Peng
    Abstract:
    This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
    Resource Type:
    Dissertation
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    University: Mohamed Amer, Sheng Chen, Liping Liu, Jun Li, Anirban Roy, Behrooz Mahasseni, Michael Lam

  • Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms

    Creator:
    Liere, Ray
    Abstract:
    We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high levels of accuracy. Most approaches to designing machine learning programs are based on the supervised learning paradigm – training examples are chosen randomly and given...
    Resource Type:
    Dissertation
    Full Text:
    Bylander, Ellen Voorhees, Marti Hearst, Efthimis Efthimiadis, Susan Dumais, Michael Kearns, David Evans

  • Exploiting monotonicity via logistic regression in Bayesian network learning

    Creator:
    Restificar, Angelo C., Dietterich, Thomas Glen, and Oregon State University. Department of Computer Science
    Abstract:
    An important challenge in machine learning is to find ways of learning quickly from very small amounts of training data. The only way to learn from small data samples is to constrain the learning process by exploiting background knowledge. In this report, we present a theoretical analysis on the use...
    Resource Type:
    Technical Report
    Full Text:
    - tions, one for each row:Phigh =P (BP |BM = high) andPlow =P (BP |BM = low). Recall that the conditional

  • Lamb Wave Mode Decomposition and Its Applications in Structural Health Monitoring

    Creator:
    Zoubi, Ahmad Bassil
    Abstract:
    Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the...
    Resource Type:
    Dissertation

  • Parsing with Recurrent Neural Networks

    Creator:
    Cross, James Henry, III
    Abstract:
    Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms and part-of-speech labels, which are considered completely distinct from each other. Recently however, the advent of dense feature representations coupled with deep learning techniques has...
    Resource Type:
    Dissertation
    Full Text:
    Juneki Hong. I am also in eternal debt to my master’s degree advisers Irina Gladkova and Michael

  • Structured gradient boosting

    Creator:
    Parker, Charles (Charles Lincoln)
    Abstract:
    The goal of many machine learning problems can be formalized as the creation of a function that can properly classify an input vector, given a set of examples of that function. While this formalism has produced a number of success stories, there are notable situations in which it fails. One...
    Resource Type:
    Dissertation
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    joint probability distribution P (x,y), which includes interactions within the observed structure x

  • 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
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    TaskTracer project. Jed Irvine, Kevin Johnsrude, Simone Stumpf and Michael Slater have made significant

  • Predictive mapping of landtype association maps in three Oregon national forests

    Creator:
    Peterman, Wendy L.
    Abstract:
    This study explores the use of predictive mapping techniques in developing Landtype Association (LTA) maps for use in natural resource management. These maps are produced for the USDA Forest Service on a regional basis at a 1:100,000 scale. The goal of this study is to develop and test a method...
    Resource Type:
    Masters Thesis
    Full Text:
    contribution of detailed Ochoco data sets, and Todd Reinwald, Terry Craig and the dedicated scientists who

  • Machine learning for improving the quality of citizen science data

    Creator:
    Yu, Jun
    Abstract:
    Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad to be performed by a limited number of trained scientists. Although citizen scientists can contribute...
    Resource Type:
    Dissertation
    Full Text:
    (Xi ·α) P (Zi|Xi;α) = oZii (1− oi) 1−Zi (3.1) The detection component captures the conditional

  • Rare category detection using hierarchical mean shift

    Creator:
    Vatturi, Pavan Kumar
    Abstract:
    Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
    Resource Type:
    Masters Thesis
    Full Text:
    continuous mapping p(x) : R2 → R. In scale space theory, p(x) is embedded into a continuous family P (x, σ

  • Image Based Crypto

    Creator:
    Basa, Gungor
    Abstract:
    The ability to create reproducible cryptographically secure keys from temporal environments (e.g., images) has the potential to be a contributor to effective cryptographic mechanisms. Due to the noisy nature of these environments, achieving this goal in a user friendly fashion is a very challenging task, especially since there exists a...
    Resource Type:
    Masters Thesis
    Full Text:
    another position based scheme. 8 Chapter 3: Notation and Preliminaries Notation: ~f , ~b, H, P , I

  • Expert-gate algorithm

    Creator:
    Joshi, Varad Vidyadhar
    Abstract:
    The goal of Inductive Learning is to produce general rules from a set of seen examples, which can then be applied to other unseen examples. ID3 is an inductive learning algorithm that can be used for the classification task. The input to the algorithm is a set of tuples of...
    Resource Type:
    Masters Thesis
    Full Text:
    denoted P and N, (also sometimes denoted as T and F) and the objects belonging to these classes are

  • Combining coordination mechanisms to improve performance in multi-robot teams

    Creator:
    Nasroullahi, Ehsan
    Abstract:
    Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can...
    Resource Type:
    Masters Thesis
    Full Text:
    policies (P ) for Episode   Episodemax do - Select a policy (Pi) using ��greedy selection - Randomly

  • Methods for cost-sensitive learning

    Creator:
    Margineantu, Dragos D.
    Abstract:
    Many approaches for achieving intelligent behavior of automated (computer) systems involve components that learn from past experience. This dissertation studies computational methods for learning from examples, for classification and for decision making, when the decisions have different non-zero costs associated with them. Many practical applications of learning algorithms, including transaction...
    Resource Type:
    Dissertation
    Full Text:
    provided the perfect environment for my graduate studies. Professors Paul Cull, Bella Bose, and Michael

  • Applying machine learning for prediction, recommendation, and integration

    Creator:
    Bao, Xinlong
    Abstract:
    This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning. FolderPredictor is an intelligent desktop software tool that helps...
    Resource Type:
    Dissertation
    Full Text:
    limited to Jed Irvine, Simone Stumpf, Michael Slater, Kevin Johnsrude, Jianqiang Shen, Lida Li, Twinkle

  • Anomaly Detection: Theory, Explanation and User Feedback

    Creator:
    Siddiqui, Md Amran
    Abstract:
    Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
    Resource Type:
    Dissertation
    Full Text:
    : Theory, Explanation and User Feedback Abstract approved: Alan P. Fern Anomaly detection has been used

  • Investigation of automatic construction of reactive controllers

    Creator:
    Westerberg, Caryl J.
    Abstract:
    In real-time control systems, the value of a control decision depends not only on the correctness of the decision but also on the time when that decision is available. Recent work in real-time decision making has used machine learning techniques to automatically construct reactive controllers, that is, controllers with little...
    Resource Type:
    Masters Thesis
    Full Text:
    on each set of training examples gave an F value of 16.37 (df.=10,25, p=0.0001). This shows that

  • Learning cost-sensitive diagnostic policies from data

    Creator:
    Zubek, Valentina Bayer
    Abstract:
    In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body tem- perature, weight, etc.) and laboratory measurements (white blood count, CT scan,...
    Resource Type:
    Dissertation
    Full Text:
    Computer Science department, my professors over the years who taught me so much: Paul Cull, Michael Quinn

  • Representationally Robust and Scalable Learning over Relational Databases

    Creator:
    Picado Leiva, Jose Manuel
    Abstract:
    Learning novel concepts from relational databases is an important problem with applications in several disciplines, such as data management, natural language processing, and bioinformatics. For a learning algorithm to be effective, the input data should be clean and in some desired representation. However, real-world data is usually heterogeneous – the...
    Resource Type:
    Dissertation
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    (three MDs) dataset while increasing the number of positive and negative (#P, #N) training examples.125

  • Emerging Technologies in Fisheries Science: A Transdisciplinary Report

    Creator:
    Firl, Alrik, Haven, Katlyn, and Newton, Samm
    Abstract:
    The Pacific Coast Groundfish Fishery harvests a diverse and large grouping of fishes, but it did not become heavily fished until around WWII. This makes the groundfish fishery a comparatively young fishery. Despite its youth, it is one of the largest and most lucrative fisheries in Oregon—with a current harvest...
    Resource Type:
    Research Paper

  • Monte-Carlo planning for probabilistic domains

    Creator:
    Bjarnason, Ronald V.
    Abstract:
    This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyze how these algorithms can resolve some of the shortcomings of traditional probabilistic planning methods. The direct policy search...
    Resource Type:
    Dissertation
    Full Text:
    probability distributions over states, specifying P (s′|s, a), where s′ is the state resulting from taking

  • Learning ranking functions for efficient search

    Creator:
    Xu, Yuehua
    Abstract:
    This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for...
    Resource Type:
    Dissertation
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    of world states W in terms of a set of predicate symbols P , action types Y , and constants C. A