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 20 of 362 for “"Active learning"”.
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Active learning for data streams.
… expensive, and time consuming to obtain. Two learning paradigms have been used by machine learning community to diminish the need for labels in training data: semi-supervised learning (SSL) and active learning (AL). AL is a reliable way to efficiently building up training sets with minimal …
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Active Learning with Combinatorial Coverage
Active learning is a practical field of machine learning as labeling data or determining which data to label can be a time consuming and inefficient task. Active learning automates the process of selecting which data to label, but current methods are heavily model reliant. This has led to the …
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Robust Domain Adaptation Using Active Learning
Traditional machine learning algorithms assume training and test datasets are generated from the same underlying distribution, which is not true for most real-world datasets. As a result, a model trained on the training dataset fails to produce good classification accuracy on the test dataset. One …
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Active Learning Within Athletic Training Education
… survey study was to explore the prevalence of active learning methods within professional athletic training education programs (ATP). In addition, this study also examined the relationship between the active learning methods and the specific athletic training learning outcome of the Board of …
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COAL : a continuous active learning system
… To achieve this, we mapped the task to a machine learning problem. The solution is two fold: learn a non linear model that can map the attribute space to the human response, which can then be used to make predictions, and an active learning strategy that enables learning this model incrementally. …
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Active learning using meta-learned priors
Deep learning models have found enormous success across a variety of displines, but training the parameters within these models generally requires huge amounts of labelled data. One technique to reduce the burden of this data requirement is meta-learning, which involves extracting knowledge from …
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Active Learning in Undergraduate (Micro)Biology Courses
… microbiology course redesigned to integrate active learning within a combined lab-lecture format. The study was grounded in Vygotsky's theory of social constructivism and examined how this environment shaped student engagement, conceptual understanding, confidence, and retention. Data came …
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Model-based active learning in hierarchical policies
… and tedious. The hierarchical reinforcement learning (HRL) field has produced elegant ways of decomposing policies and value functions using semi-Markov decision processes. However, there is still a lack of demonstrations in larger nonlinear systems with discrete and continuous variables. To …
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Efficient Bayesian active learning and matrix modelling
… collecting supervised labels can be costly. Active learning addresses this by selecting, sequentially, only the most useful data in light of the information collected so far. The online nature of such algorithms often necessitates efficient computations. Thus, we present a framework for …
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Technology Enabled Active Learning : system architecture & implementation
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2001.
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Program Inference and Regeneration via Active Learning
Software now plays a central role in numerous aspects of human society. Current software development practices involve significant developer effort in all phases of the software life cycle, including the development of new software, detection and elimination of defects and security vulnerabilities …
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Teaching concepts utilizing active learning computer environments
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2001.
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Predictive analytics of active learning based education
Learning Analytics (LA) is defined as the collection, measurement, and analysis of data related to student performance such that the feedback from the analytical insights can be used to optimize student learning and improve student outcomes. Blended Learning (BL) is a teaching paradigm that …
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Structural Priors for Active Learning on Robots
… data on a real robot is slow and expensive. Active learning, in which the learner chooses the data that will best accelerate learning, has been shown to reduce data requirements in machine learning and statistics applications, but has seen limited application to real robots. This thesis …
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Active Learning Under Limited Interaction with Data Labeler
Active learning (AL) aims at reducing labeling effort by identifying the most valuable unlabeled data points from a large pool. Traditional AL frameworks have two limitations: First, they perform data selection in a multi-round manner, which is time-consuming and impractical. Second, they usually …
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Advances in Active Learning and Sequential Decision Making
Much of the recent success of machine learning methods was enabled by exploiting the wealth of labeled data produced in the past few years. However, for several important real-world applications such large-scale data collection is still infeasible. This includes areas such as robotics, healthcare, …
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Evaluating the Effectiveness of Active Learning in Introductory Physics
… students learn than in a traditional lecture. Learning gains can be much more significant when students are actively engaged in the material and have the opportunity to receive meaningful feedback from instructors (Hake R. R., 1997). Some techniques utilized by science educators to encourage …
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LLM-powered active learning for cost-effective text classification
This thesis presents an LLM-powered active learning framework for cost-effective text classification, addressing the challenge of potential LLM annotation errors while balancing annotation quality and model accuracy. Our methodology combines human and large language model (LLM) annotations using …
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Active learning based on a hybrid neural network modeller
… model), they can be complementary in the entire learning system, and therefore effectively enhance the entire learning system’s efficiency and accuracy. For learning, the neural networks attempt to actively select the most informative and important training data.<br/><br/>The methods described in …
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Deep adaptive anomaly detection using an active learning framework
… the anomaly detection outcome? We employ a Deep Learning and an Active Learning framework to learn features for anomaly detection. In Active Learning, an Oracle (usually a domain expert) labels a small amount of data over a series of training rounds. The deep neural network is trained after each …
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