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Full-Text Articles in Engineering
Cost-Sensitive Learning-Based Methods For Imbalanced Classification Problems With Applications, Talayeh Razzaghi
Cost-Sensitive Learning-Based Methods For Imbalanced Classification Problems With Applications, Talayeh Razzaghi
Electronic Theses and Dissertations
Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the …
Sketchart: A Pen-Based Tool For Chart Generation And Interaction., Andres Vargas Gonzalez
Sketchart: A Pen-Based Tool For Chart Generation And Interaction., Andres Vargas Gonzalez
Electronic Theses and Dissertations
It has been shown that representing data with the right visualization increases the understanding of qualitative and quantitative information encoded in documents. However, current tools for generating such visualizations involve the use of traditional WIMP techniques, which perhaps makes free interaction and direct manipulation of the content harder. In this thesis, we present a pen-based prototype for data visualization using 10 different types of bar based charts. The prototype lets users sketch a chart and interact with the information once the drawing is identified. The prototype's user interface consists of an area to sketch and touch based elements that will …
Taming Wild Faces: Web-Scale, Open-Universe Face Identification In Still And Video Imagery, Enrique Ortiz
Taming Wild Faces: Web-Scale, Open-Universe Face Identification In Still And Video Imagery, Enrique Ortiz
Electronic Theses and Dissertations
With the increasing pervasiveness of digital cameras, the Internet, and social networking, there is a growing need to catalog and analyze large collections of photos and videos. In this dissertation, we explore unconstrained still-image and video-based face recognition in real-world scenarios, e.g. social photo sharing and movie trailers, where people of interest are recognized and all others are ignored. In such a scenario, we must obtain high precision in recognizing the known identities, while accurately rejecting those of no interest. Recent advancements in face recognition research has seen Sparse Representation-based Classification (SRC) advance to the forefront of competing methods. However, …