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 56 for “"distributed learning"”.
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Distributed learning in games under bounded rationality
… considers models, solution concepts, and learning dynamics that explicitly embrace bounded rationality as a structural feature. Furthermore, we focus on the inherently distributed nature of these systems, where agents make decisions independently based on local information and …
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Breaking the dimension dependence in distributed learning
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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Git-based platform for distributed learning communities
Instructors use software called learning management systems to administer online courses. Traditionally these digital platforms mimic physical classrooms, but technology opens up potential for new types of interactions. In this thesis, we develop a learning management system on top of Git to create …
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Architecture design and simulation for distributed learning classifier systems
In this thesis, we introduce the Distributed Learning Classifier System (DLCS) as a novel extension of J. H. Holland's standard learning classifier system. While the standard LCS offers effective real-time control and learning, one of its limitations is that it does not provide a mechanism for …
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Distributed learning automata based data dissemination in swarm robotic systems
… delivery latency while consuming minimal energy. Learning automata are a form of Reinforcement Learning that is computationally inexpensive and can adapt to a dynamic environment. This combination allows for lightweight decision making based on the current topology of the network. We present two …
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A platform for distributed learning and teaching of algorithmic concepts
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Efficient algorithms for distributed learning, optimization and belief systems over networks
A distributed system is composed of independent agents, machines, processing units, etc., where interactions between them are usually constrained by a network structure. In contrast to centralized approaches where all information and computation resources are available at a single location, agents …
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Prediction modeling and distributed learning for radiotherapy outcomes in lung cancer patients
Contains fulltext : 205662.pdf (Publisher’s version ) (Open Access)
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Computer-supported virtual collaborative learning and assessment framework for distributed learning environment
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.
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Modifications To The Fuzzy-ARTMAP Algorithm For Distributed Learning In Large Data Sets
… neural network approaches. Nevertheless the learning time of FAM can slow down considerably when the size of the training set increases into the hundreds of thousands. In this dissertation we apply data partitioning and network partitioning to the FAM algorithm in a sequential and parallel …
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Identifying At-Risk Students: An Assessment Instrument for Distributed Learning Courses in Higher Education
… software lead to corresponding gains in student learning. Educators do not yet possess sophisticated assessments of what we may be gaining or losing as we widen the scope of distributed learning. The purpose of this study was not to draw sweeping conclusions with respect to the costs or benefits …
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LIDS: An Extended LSTM Based Web Intrusion Detection System With Active and Distributed Learning
… have limitations that are typical of any machine learning system, including high false-positive rates, a lack of clear infrastructure for deployment, the requirement for data to be centralized, and an inability to add modules tailored to specific organizational threats. To address these …
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Towards secure and decentralized energy dispatch: fuel optimization using distributed learning for energy demand and storage strategies
… systems that involve constrained resources distributed across multiple collaborating subsystems, achieving optimal resource allocation while preserving data privacy and maintaining operational constraints is a challenge. Conventional centralized optimization techniques require aggregation of …
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Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications
… of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning …
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Using modular architectures within distributed learning environments : a means for improving the efficiency of instructional design & development processes
… modular architectures provide large-scale distributed learning environments with the flexibility required to meet the diverse needs of a global audience. Comprehensive standards for implementing such architectures are now being developed by several organizations. In order to take full …
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SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning
… from big data. However, traditional machine learning approaches are not well fit to analyze the full value of big data. Explicitly, current research and practice of Machine learning do not fully support some important features for big data analytics such as incremental learning, distributed …
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A Distributed Q-learning Classifier System for task decomposition in real robot learning problems
A distributed reinforcement-learning system is designed and implemented on a mobile robot for the study of complex task decomposition in real robot learning environments. The Distributed Q-learning Classifier System (DQLCS) is evolved from the standard Learning Classifier System (LCS) proposed by …
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Distributed Supervised Statistical Learning
… on a single computer. Often such data has to be distributed over multiple computers which then makes the storage, pre-processing, and data analysis possible in practice. In the age of big data, distributed learning has gained popularity as a method to manage enormous datasets. In this thesis, we …
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Toward communication-efficient and secure distributed machine learning
… recent years, there is an increasing interest in distributed machine learning. On one hand, distributed machine learning is motivated by assigning the training workload to multiple devices for acceleration and better throughput. On the other hand, there are machine-learning tasks requiring …
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Learning-based adaptive design for dynamic spectrum access in cognitive radio networks.
… networks. The main objective is designing online learning and access policies which maximize the total throughput of the secondary users in a cognitive radio network. As the first approach, we consider the auction-based formulation in design of dynamic spectrum access mechanisms where it is …
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