Index Catalog // ScholarsArchive@OSU

Statistical Properties and Guarantees for Certain Anomaly Detection Relevant Problems

Creator:: Liu, Si
Abstract:: Anomaly detection aims at detecting the points that appear different than the majority of the data, such that they are suspected to be generated from a different distribution. Anomaly detectors have been applied in many different fields, such as detecting fraudulent behaviors in bank transaction, finding broken sensors in a...
Resource Type:: Dissertation

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

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

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

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

Image features and learning algorithms for biological, generic and social object recognition

Creator:: Zhang, Wei
Abstract:: Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require...
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

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

ScholarsArchive@OSU

Statistical Properties and Guarantees for Certain Anomaly Detection Relevant Problems

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Improving automated email tagging with implicit feedback

Probabilistic models for quality control in environmental sensor networks

Applying machine learning for prediction, recommendation, and integration

Efficient training and feature induction in sequential supervised learning

Activity recognition in desktop environments

Image features and learning algorithms for biological, generic and social object recognition

Learning MDP action models via discrete mixture trees

Protein secondary structure prediction using conditional random fields and profiles

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