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Full-Text Articles in Engineering
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Michigan Tech Publications
Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, …
Deep Learning And Transfer Learning In The Classification Of Eeg Signals, Jacob M. Williams
Deep Learning And Transfer Learning In The Classification Of Eeg Signals, Jacob M. Williams
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Deep learning is seldom used in the classification of electroencephalography (EEG) signals, despite achieving state of the art classification accuracies in other spatial and time series data. Instead, most research has continued to use manual feature extraction followed by a traditional classifier, such as SVMs or logistic regression. This is largely due to the low number of samples per experiment, high-dimensional nature of the data, and the difficulty in finding appropriate deep learning architectures for classification of EEG signals. In this thesis, several deep learning architectures are compared to traditional techniques for the classification of visually evoked EEG signals. We …
From Creativity To Classification: A Logical Approach To Patent Searching, Marian G. Armour-Gemmen
From Creativity To Classification: A Logical Approach To Patent Searching, Marian G. Armour-Gemmen
Faculty & Staff Scholarship
Engineering students and professors need to understand and search intellectual property. In the past, librarians have instructed them on using the United States Patent Classification (USPC). In 2015, after a period of transition, the United States Patent and Trademark Office phased out the USPC and began exclusively classifying in the Cooperative Patent Classification (CPC). This adoption presented librarians a challenge of instructing students and professors in the easiest and most effective patent search. By tying patent searching to an example and presenting classification in an understandable fashion using CPC in conjunction with USPC, this writer presents a logical directed search …
Investigation Into The Application Of Personality Insights And Language Tone Analysis In Spam Classification, Colm Mcgetrick
Investigation Into The Application Of Personality Insights And Language Tone Analysis In Spam Classification, Colm Mcgetrick
Dissertations
Due to its persistence spam remains as one of the biggest problems facing users and suppliers of email communication services. Machine learning techniques have been very successful at preventing many spam mails from arriving in user mailboxes, however they still account for over 50% of all emails sent. Despite this relative success the economic cost of spam has been estimated as high as $50 billion in 2005 and more recently at $20 billion so spam can still be considered a considerable problem. In essence a spam email is a commercial communication trying to entice the receiver to take some positive …
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.