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 37 for “"Network learning"”.
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Development of New Cost-Sensitive Bayesian Network Learning Algorithms
Bayesian networks are becoming an increasingly important area for research and have been proposed for real world applications such as medical diagnoses, image recognition, and fraud detection. In all of these applications, accuracy is not sufficient alone, as there are costs involved when errors …
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Neural Network Learning for Time-Series Predictions Using Constrained Formulations
… with violation guided backpropagation to neural network learning for near noiseless time-series benchmarks, we achieve much improved prediction performance as compared to that of previous work, while using less parameters. For noisy time-series, such as financial time series, we have studied …
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Improved cuckoo search based neural network learning algorithms for data classification
Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural Network (BPNN) algorithm has been used as a tool for recognizing a mapping function among a known set of input and output examples. These networks can be trained with gradient descent back propagation. The algorithm is not …
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Neuromorphic deep convolutional neural network learning systems for FPGA in real time
Deep Learning algorithms have become one of the best approaches for pattern recognition in several fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks stand out, due to their …
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Students’ perceptions of the practice firms network learning environment in Brazil : a phenomenographic approach
… research deals with how students of a particular learning environment in management education, which I call here the Practice Firms Network Learning Environment (PFN), describe their relationship with this learning environment. In the PFN model students conceptualise, design and sell ‘virtual’ …
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48 Children, 2 Teachers, 1 Classroom, and 4 Computers: A Personal Exploration of a Network Learning Environment
… the NLE was a mediating factor for numerous learning activities. For the teachers, their images of teaching and technology shaped this innovation. This study also revealed timely concerns about the access and efficacy of technology in elementary classrooms.
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Collaborative Learning and the Co-design of Corporate Responsibility. Building a Theory of Multi-Stakeholder Network Learning from Case Studies of Standardization in Corporate Responsibility.
… standards from the perspective of organisational learning theory. The author proposes that standards development projects can be understood as Network Learning episodes where learning is reflected in changes in structures, interpretations and practices accompanied by learning processes. Network …
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Characterizing the Energy Requirement of Computer Vision
The energy requirements of neural network learning are growing at a rapid rate. Increased energy demands have caused a global need to seek ways to improve energy efficiency of neural network learning. This thesis aims to establish a baseline on how adjusting basic parameters can affect energy …
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How firms learn about new product development in their business networks
… about new product development (NPD) network learning. It addresses the following research questions: how is business network learning processed in NPD? How do firms engage with their business alliances in the NPD network learning process? How does the network learning mechanism impact …
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A Bayesian Network Approach to the Self-organization and Learning in Intelligent Agents
A Bayesian network approach to self-organization and learning is introduced for use with intelligent agents. Bayesian networks, with the help of influence diagrams, are employed to create a decision-theoretic intelligent agent. Influence diagrams combine both Bayesian networks and utility theory. …
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New algorithm for neural network data discrimination applied to Markarian 421 high energy gamma rays
A new neural network learning algorithm, called the Umbrella Algorithm, is developed and analysed. Its generalization, which does not exhibit over-specialisation, is observed in the EXOR problem and in an artificial data discrimination (Toy Data) problem. The learning time is found to be about 1/15 …
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Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence.
… work investigates the use of evolved Bayesian networks learning algorithms based on computational intelligence meta-heuristic algorithms. These algorithms are applied to a new domain provided by the exclusive data, available to this project from an industry partnership with ODS-Petrodata, a …
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Applications of Bayesian networks in natural hazard assessments
… all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical …
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PACKET FILTER APPROACH TO DETECT DENIAL OF SERVICE ATTACKS
… quantity of packets or connections to crash its network resources, bandwidth, equipment, or servers. Packet filtering methods are the most known way to prevent these attacks via identifying and blocking the spoofed attack from reaching its target. In this project, the extent of the DoS attacks …
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Bayesian nonparametric learning for complicated text mining
… or under-fitting issues. Bayesian nonparametric learning is a key approach for learning the number of mixtures in a mixture model (also called the model selection problem), and has emerged as an elegant way to handle a flexible number of topics. The core idea of Bayesian nonparametric models is …
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Information fusion schemes for real time risk assessment in adaptive control systems
… Flight Control System (IFCS) deploys a neural network for in-flight aircraft failure accommodation. Verification and validation (V&V) of adaptive systems is a challenging research problem. Our approach to V&V relies on real-time monitoring of neural network learning. Monitors detect learning …
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Learning, Reasoning, and Planning with Relational and Temporal Neural Networks
… an overview of a neuro-symbolic framework for learning, reasoning, and planning with relational and temporal neural networks. The key idea is to exploit a structural bias in neural network learning that enables us to describe complex relational-temporal events and actions. These structures form …
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Designing "learning organizations" : a critical evaluation of the strategies and policies proposed in the literature
Designing organizations with enhanced learning capability is a leitmotif of current approaches to organizational design and has implications for the way organizations are managed with respect to education, training, and development. The aim of this work is the identification of the factors and …
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Neural network based modeling and data mining of blast furnace operations
… One such method, the Artificial Neural Network (ANN), has been successfully used in many areas. ANNs offer a data driven approach which has potential to produce a more accurate, flexible model in less time and at a lower cost. This model can then be used for simulation, prediction, and …
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