Index Catalog // ScholarsArchive@OSU

The Familiarity Hypothesis: Explaining the Behavior of Deep Learning Methods in Out-of-Distribution Detection

Creator:: Guyer, Alexander
Abstract:: Many object recognition applications require detecting and responding to objects drawn from a different distribution from that of the training data. This task is referred to as out-of-distribution (OOD) detection, and it is often formulated as an outlier detection problem wherein the probability distribution of the known data P(X) is...
Resource Type:: Masters Thesis

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:: Dodge, Jonathan E.
Abstract:: Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
Resource Type:: Dissertation

A Benchmarking Study of Unsupervised Anomaly Detection Algorithms

Creator:: Emmott, Andrew F.
Abstract:: It is common practice in the unsupervised anomaly detection literature to create experimental benchmarks by sampling from existing supervised learning datasets. We seek to improve this practice by identifying four dimensions important to real-world anomaly detection applications --- point difficulty, clusteredness of anomalies, relevance of features, and relative frequency of...
Resource Type:: Masters Thesis

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Creator:: Zemicheal, Tadesse H.
Abstract:: Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...
Resource Type:: Dissertation

Machine learning methods for public policy : simulation, optimization, and visualization

Creator:: McGregor, Sean
Abstract:: Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for...
Resource Type:: Dissertation

Monte Carlo Tree Search with Fixed and Adaptive Abstractions

Creator:: Hostetler, Jesse A.
Abstract:: Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
Resource Type:: Dissertation

Simulator-Defined MDP Planning with Applications in Natural Resource Management

Creator:: Alkaee Taleghan, Majid
Abstract:: This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...
Resource Type:: Dissertation

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

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

Creating, Understanding and Applying Machine Learning Models of Multiple Species

Creator:: Griffioen, Arwen Twinkle E.
Abstract:: Many problems in ecology and conservation biology can be formulated and solved using machine learning algorithms for multi-label classification. This dissertation addresses three topics related to predicting the distributions of multiple species. It improves existing methods and proposes a new modeling paradigm to address the multi-species, multi-label problem. The first...
Resource Type:: Dissertation

Modeling of Chinook Salmon Runs on the North Umpqua River

Creator:: Jordan, Scott
Abstract:: To model the behavior of the spring run Chinook salmon, we expanded the current dataset of salmon counts at the Winchester Dam, Winchester, Oregon, to include historical data from 1991 through 1997. These counts were used to identify Chinook salmon behavior that resulted in defining preferable water conditions during their...
Resource Type:: Research Paper and Presentation

Column Time Warping with Neighborhood Distortion Cost

Creator:: Kucey, Brennan Mark
Abstract:: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
Resource Type:: Honors College Thesis

Comparison of machine learning methods for predicting bird distributions

Creator:: Bikkina, Vinay
Abstract:: The purpose of this study is to explore kernel machine learning methods for species distribution modeling. Previous studies have shown the success of Generalized Boosted Regression Models, however kernel methods have been unexplored for species distribution modeling. Using the eBird dataset, four machine learning methods were tested for accuracy and...
Resource Type:: Honors College Thesis

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

Probabilistic models for quality control in environmental sensor networks

Creator:: Dereszynski, Ethan W.
Abstract:: Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
Resource Type:: Dissertation

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

Efficient training and feature induction in sequential supervised learning

Creator:: Hao, Guohua
Abstract:: Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
Resource Type:: Dissertation

Evaluating online text classification algorithms for email prediction in TaskTracer

Creator:: Keiser, Victoria L.
Abstract:: This paper examines how six online multiclass text classification algorithms perform in the domain of email tagging within the TaskTracer system. TaskTracer is a project-oriented user interface for the desktop knowledge worker. TaskTracer attempts to tag all documents, web pages, and email messages with the projects to which they are...
Resource Type:: Masters Thesis

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

Learning MDP action models via discrete mixture trees

Creator:: Wynkoop, Michael S.
Abstract:: This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
Resource Type:: Masters Thesis

A probabilistic model for anomaly detection in remote sensor streams

Creator:: Dereszynski, Ethan W.
Abstract:: Remote sensors are becoming the standard for observing and recording ecological data in the field. Such sensors can record data at fine temporal resolutions, and they can operate under extreme conditions prohibitive to human access. Unfortunately, sensor data streams exhibit many kinds of errors ranging from corrupt communications to partial...
Resource Type:: Masters Thesis

Model-based approximation methods for reinforcement learning

Creator:: Wang, Xin
Abstract:: The thesis focuses on model-based approximation methods for reinforcement learning with large scale applications such as combinatorial optimization problems. First, the thesis proposes two new model-based methods to stablize the value–function approximation for reinforcement learning. The first one is the BFBP algorithm, a batch-like reinforcement learning process which iterates between...
Resource Type:: Dissertation

Protein secondary structure prediction using conditional random fields and profiles

Creator:: Shen, Rongkun
Abstract:: Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
Resource Type:: Masters Thesis

Constraining Bayesian network learning with qualitative models

Creator:: Altendorf, Eric E.
Abstract:: Machine learning encompasses probabilistic and statistical techniques that can build models from large quantities of extensional information (examples) with minimal dependence on intensional information (domain knowledge). This focus of machine learning is reflected in the never-ending quest for "off-the-shelf" classifiers. To generalize to unseen data, however, we must make use...
Resource Type:: Masters Thesis

Leaf shape recognition via support vector machines with edit distance kernels

Creator:: Sinha, Shriprakash
Abstract:: Edit distances are a well-established technique for classification problems. They have been employed successfully in many classification problems including chromosome classification and hand-written digit recognition. Virtually all machine learning algorithms represent the objects to be classified as vectors of features. However, edit distances provide only a measure of the difference...
Resource Type:: Masters Thesis

Sequential supervised learning and conditional random fields

Creator:: Ashenfelter, Adam J.
Abstract:: Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all examples are independent from one another. The order of the examples contains no information. Nonetheless, many problems have a sequential nature. Classifiers for these problems...
Resource Type:: Masters Thesis

Demosaicking of color images by means of conditional random fields

Creator:: Kelm, Bernd Michael
Abstract:: Modern digital still cameras are equipped with just a single CCD for color image acquisition. Since only one spectral band can be recorded in each pixel, a mosaic of red, green and blue color filters is placed in front of the chip. The process of subsequently calculating a full color...
Resource Type:: Masters Thesis

Calibrating recurrent sliding window classifiers for sequential supervised learning

Creator:: Joshi, Saket Subhash
Abstract:: Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part-of-speech tagging and text-to-speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (RSW) classifier, which maps some window of the input sequence plus some number...
Resource Type:: Masters Thesis

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

Three methods for estimating subpixel cover fractions in coarse resolution imagery

Creator:: Damon, Diane L.
Abstract:: Coarse resolution imagery, such as that produced by the MODIS instrument, poses the challenge of estimating sub-pixel proportions of di erent land cover types. This problem is di cult because of the variety and variability of vegetation within individual pixels. This thesis describes and compares two existing algorithms for estimating...
Resource Type:: Masters Thesis

Evaluation functions in aerial image segmentation

Creator:: Langford, William T.
Abstract:: Remote sensing is the most practical way to acquire large amounts of land cover data for monitoring and understanding environmental change, so it is important to be able to map land cover from imagery. Maps defining land cover patches as polygons rather than pixels greatly improve processing efficiency in models...
Resource Type:: Dissertation

Shrunken learning rates do not improve AdaBoost on benchmark datasets

Creator:: Forrest, Daniel L. K.
Abstract:: Recent work has shown that AdaBoost can be viewed as an algorithm that maximizes the margin on the training data via functional gradient descent. Under this interpretation, the weight computed by AdaBoost, for each hypothesis generated, can be viewed as a step size parameter in a gradient descent search. Friedman...
Resource Type:: Masters Thesis

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

Grasshopper Infestation Prediction : An Application of Data Mining to Ecological Modeling

Creator:: Bunjongsat, Waranun
Abstract:: This thesis presents a case study of applying machine learning tools to build a predictive model of annual infestations of grasshoppers in Eastern Oregon. The purpose of the study was two-fold. First, we wanted to develop a predictive model. Second, we wanted to explore the capabilities of existing machine learning...
Resource Type:: Capstone Project

Just enough die-level functional test : optimizing IC test via machine learning and decision theory

Creator:: Fountain, Tony R.
Abstract:: This research explores the hypothesis that methods from decision theory and machine learning can be combined to provide practical solutions to current manufacturing control problems. This hypothesis is explored by developing an integrated approach to solving one manufacturing problem - the optimization of die-level functional test. An integrated circuit (IC)...
Resource Type:: Dissertation

Reinforcement learning for job-shop scheduling

Creator:: Zhang, Wei, 1960-
Resource Type:: Dissertation

Understanding and improving error-correcting output coding

Creator:: Kong, Eun Bae
Abstract:: Error-correcting output coding (ECOC) is a method for converting a k-classsupervised learning problem into a large number L of two-class supervised learningproblems and then combining the results of these L evaluations. Previous researchhas shown that ECOC can dramatically improve the classi cation accuracy of supervisedlearning algorithms that learn to classify...
Resource Type:: Dissertation

A study of distance-based machine learning algorithms

Creator:: Wettschereck, Dietrich
Abstract:: Distance-based algorithms are machine learning algorithms that classify queries by computing distances between these queries and a number of internally stored exemplars. Exemplars that are closest to the query have the largest in uence on the classi cation assigned to the query. Two speci c distance-based algorithms, the nearest neighbor...
Resource Type:: Dissertation

Classification context in a machine learning approach to predicting protein secondary structure

Creator:: Langford, Bill T.
Abstract:: An important problem in molecular biology is to predict the secondary structure of proteins from their primary structure. The primary structure of a protein is the sequence of amino acid residues. The secondary structure is an abstract description of the shape of the folded protein, with regions identified as alpha...
Resource Type:: Masters Thesis

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

Concept coverage and its application to two learning tasks

Creator:: Almuallim, Hussein Saleh
Abstract:: The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size for given accuracy and confidence parameters. This thesis begins by asking whether good learning algorithms can be designed by maximizing their coverage. There are three questions...
Resource Type:: Dissertation

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

Correct abstraction in counter-planning : a knowledge compilation approach

Creator:: Flann, Nicholas S.
Abstract:: Knowledge compilation improves search-intensive problem-solvers that are easily specified but inefficient. One promising approach improves efficiency by constructing a database of problem-instance/best-action pairs that replace problem-solving search with efficient lookup. The database is constructed by reverse enumeration- expanding the complete search space backwards, from the terminal problem instances. This approach...
Resource Type:: Dissertation

Converting English text to speech : a machine learning approach

Creator:: Bakiri, Ghulum
Abstract:: The task of mapping spelled English words into strings of phonemes and stresses ("reading aloud") has many practical applications. Several commercial systems perform this task by applying a knowledge base of expert-supplied letter-to-sound rules. This dissertation presents a set of machine learning methods for automatically constructing letter-to-sound rules by analyzing...
Resource Type:: Dissertation

An empirical study into learning through experimentation

Creator:: Ruff, Ritchey Alvin
Abstract:: A key aspect of how we understand the world revolves around an ability to manipulate our surroundings to experiment. From the scientific method through theories of child development, the ability to experiment is deemed critical; however, few studies have been performed to understand the strengths and weaknesses of different experimental...
Resource Type:: Dissertation

An implementation and initial test of generalized radial basis functions

Creator:: Wettschereck, Dietrich
Abstract:: Generalized Radial Basis Functions were used to construct networks that learn input-output mappings from given data. They are developed out of a theoretical framework for approximation based on regularization techniques and represent a class of three-layer networks similar to backpropagation networks with one hidden layer. A network using Gaussian base...
Resource Type:: Masters Thesis

Relationship of pauses to problem solving events in mechanical design protocols

Creator:: Chamberlin, Martha J.
Abstract:: This thesis compares two methods for studying the problem-solving processes of mechanical design engineers. The first method, verbal protocol analysis, was applied by L. Stauffer to construct a problem-solving model of mechanical design. The second method, timing analysis, measures the time intervals separating drawing or speaking actions during the design...
Resource Type:: Masters Thesis

A specialized ATMS for equivalence relations

Creator:: Koff, Caroline Nan
Abstract:: A specialized ATMS for efficiently computing equivalence relations in multiple contexts is introduced. This specialized ATMS overcomes the problems with existing solutions to reasoning with equivalence relations. The most direct implementation of an equivalence relation in the ATMS-encoding the reflexive, transitive, and symmetric rules in the consumer architecture-produces redundant equality...
Resource Type:: Masters Thesis

An investigation of the search space of Lenat's AM program

Creator:: Mishra, Prafulla K.
Abstract:: AM is a computer program written by Doug Lenat that discovers elementary mathematics starting from some initial knowledge of set theory. The success of this program is not clearly understood. This work is an attempt to explore the search space of AM in order to understand the success and eventual...
Resource Type:: Masters Thesis

Disjunctions in forward chaining logic programming

Creator:: Corpron, Daniel R.
Abstract:: A forward-chaining logic programming system (FORLOG) has been developed at Oregon State University. This system coupled with an assumption-based truth maintenance system (ATMS), provides an alternative to the logic programming paradigm of backward-chaining with Horn clauses. To compare FORLOG to this paradigm, we define a subset of FORLOG, called mini-FORLOG,...
Resource Type:: Masters Thesis

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