Search for dissertations about: "Incremental learning"
Showing result 1 - 5 of 53 swedish dissertations containing the words Incremental learning.
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1. Incremental Clustering of Source Code : a Machine Learning Approach
Abstract : Technical debt at the architectural level is a severe threat to software development projects. Uncontrolled technical debt that is allowed to accumulate will undoubtedly hinder speedy development and maintenance, introduce bugs and problems in the software product, and may ultimately result in the abandonment of the source code. READ MORE
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2. Incremental Learning and Testing of Reactive Systems
Abstract : This thesis concerns the design, implementation and evaluation of a specification based testing architecture for reactive systems using the paradigm of learning-based testing. As part of this work we have designed, verified and implemented new incremental learning algorithms for DFA and Kripke structures. READ MORE
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3. A study of wireless communications with reinforcement learning
Abstract : The explosive proliferation of mobile users and wireless data traffic in recent years pose imminent challenges upon wireless system design. The trendfor wireless communications becoming more complicated, decentralized andintelligent is inevitable. READ MORE
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4. Interoperability Infrastructure and Incremental learning for unreliable heterogeneous communicating Systems
Abstract : In a broader sense the main research objective of this thesis (and ongoing research work) is distributed knowledge management for mobile dynamic systems. But the primary focus and presented work focuses on communication/interoperability of heterogeneous entities in an infrastructure less paradigm, a distributed resource manipulation infrastructure and distributed learning in the absence of global knowledge. READ MORE
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5. Reliable and Efficient Distributed Machine Learning
Abstract : With the ever-increasing penetration and proliferation of various smart Internet of Things (IoT) applications, machine learning (ML) is envisioned to be a key technique for big-data-driven modelling and analysis. Since massive data generated from these IoT devices are commonly collected and stored in a distributed manner, ML at the networks, e.g. READ MORE