Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 19 of 19 for “"labeled dataset"”.
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Speech processing with less supervision : learning from weak labels and multiple modalities
… network models and vast quantities of in-domain labeled data. However, collecting a labeled dataset covering all domains can be either expensive due to the diversity of speech or almost impossible for some tasks such as speech-to-speech translation. Such a paradigm limits the applicability of …
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Hyperpartisanship in Web Searched Articles
… or not. The methods were developed using a labeled dataset which was released as a part of the SemEval Task 4 - Hyperpartisan News Detection. The model was applied to queries related to U. S. midterm elections in 2018. We found that more than half the articles in web search queries showed …
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Self-Training and Calibration for Learning with Limited Data
… such as self-training are able to leverage unlabeled data, which is widely available, as opposed to only using labeled data like many successful supervised learning methods. One part of self-training is to use a trained model to create pseudo-labels for unlabeled data and then select some of …
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Classification of computer programs in the Scratch online community
… by type. This effort included constructing a labeled dataset of 873 Scratch projects and their corresponding types, to be used for training a supervised classifier model. This dataset was constructed through a collective process of consensus-based annotation by experts. To realize the goal of …
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A Machine Learning Model for Understanding How Users Value Designs: Applications for Designers and Consumers
… test this framework, generating a “bottom-up”, labeled dataset from the feedback, requiring no post-processing. Finally, I develop methods for the computational analysis of this data. The analysis is based on a probabilistic ML model trained on the real user data collected. The model is trained …
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Self-training for cyberbully detection: Achieving high accuracy with a balanced multi-class dataset
… involves the meticulous curation of a balanced dataset specifically designed for training the ML/ DL models. To overcome the challenge of limited labeled data, we employ a semi-supervised self-training algorithm, which effectively expands the size of the labeled dataset. By leveraging real-world …
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The surprising effectiveness of explicit semantic analysis in dataless classification
… not always be possible to create a sizeable labeled dataset for every scenario or domain of interest. Thus, techniques like “Dataless Classification” have been proposed in the past that are able to bootstrap the creation of a classifier by only requiring semantic descriptions of the labels. …
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Improving the temporal consistency of satellite-based contrail detections using ensemble Kalman filtering
… not only reduce the quality of such a dataset, but risk introducing biases in the computed properties. We use an existing deep-learning based contrail detector which as of now presents temporal inconsistencies that make tracking challenging. We address this issue by post-processing the …
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Improving prediction of opioid use disorder with machine learning algorithms
… learning classification algorithms. To build a labeled dataset, responses from the 2018 and 2019 edition of the National Survey on Drug Use and Health (NSDUH) were collected. This dataset was used to train and test several classification models (Artificial Neural Network, Naïve Bayes and …
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Deep Learning Based Crop Row Detection
… task. The reasons include paucity of large scale labeled datasets in this domain, diversity in crops, and the diversity of appearance of the same crops at various stages of their growth. In this work, we discuss the development of a practical real-life crop row detection system in collaboration …
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Fast Supervised Annotation and Active Learning with Uncertainty for Cloud Mask Dataset Generation
… inertia due to difficulty generating robust, labeled datasets for complex learners. Variation in data and diverse tasks make it difficult to both generally crowd source to build such datasets, and to offload this responsibility to the small number of expert annotators that exist. Currently, no …
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Label-Efficient Visual Understanding with Consistency Constraints
… tasks, as long as a sufficiently large labeled dataset is available during the training time. However, the progress of these visual tasks is limited by the number of manual annotations. On the other hand, it is usually time-consuming and error-prone to annotate visual data, rendering the …
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Machine learning for network traffic classification under labeled data and training time constraints
… classification when constrained by too little labeled data or insufficient time to train models from scratch. Network traffic classification is essential in network security, network management, and application identification. Labeling network traffic data, however, is often time-consuming and …
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Continuous Personalized Fall Detection and Data Collection
… fall detection that utilizes only a simulated dataset cannot be recommended for real-world application as it does not reflect the characteristics of fall from the target population. We propose a solution that will move fall detection into the real world by being capable of collecting real data …
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Accelerating the Design Process Through Natural Language Processing-based Idea Filtering
… accurately evaluating and filtering our textual dataset. Furthermore, we observe that the model accuracy improves with the training data size with diminishing marginal effect. The findings can facilitate informed decision making regarding the trade-off between model accuracy and manual labeling …
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Explainable AI for Social Good: Applications in Mental Health, Public Health Risk, and Environmental Traceability
… are: 1. It tackles the lack of a fine-grained labeled dataset for Reddit that extends beyond topic-specific subreddits by first curating a labeled dataset and then employing an active learning strategy to help with the training; 2. It proposes a novel multi-task learning model, AMMNet, that …
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Data-Driven Detection of En-route Convective Weather Avoidance and Development of a Weather Assessment Model
… machine learning approach, with training data labeled by crowd-sourced subject matter experts, generally airline pilots. By leveraging transfer learning techniques, a small labeled dataset was sufficient to train the deviation detection classifier. After the deviation detection classifier was …
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Action Recognition with Knowledge Transfer
… learning: training a model on a large-scale labeled dataset (source) and then fine-tuning the model on the small-scale labeled datasets (targets). However, existing action recognition models do not always generalize well on new tasks or datasets because of the following two reasons. i) …
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A study of remote sensing based natural and built environment monitoring: from fully supervised to weakly supervised learning
… data. More specifically, there are very few RS datasets that provide pixel-level annotations. If one aims to perform image-based segmentation using a fully supervised approach, two methods have been adopted, as demonstrated in my previous research endeavors described above: (1) the use of a …