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 25 for “"Inductive learning"”.
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The evolution of 'Boxes' to quantized inductive learning: a study in inductive learning
An inductive learning method is analyzed for use in on-line control. The controller has the benefit of being designed without a system model and is able to adapt itself to varying system parameters. Numerical experiments were performed with the Quantized Inductive Learning (QIL) algorithm, an …
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Damage identification using inductive learning
… identification method incorporating the use of inductive learning is presented. Inductive learning is the process of learning from examples. The method utilizes as much dynamic-response data as is available, ordering this information to find the best data with which to discriminate among a set …
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The effects of variability on damage identification with inductive learning
… affect the structure’s performance. Inductive learning is one tool which has been proposed as an effective method to perform damage identification. There are many variabilities which are inherent in damage identification and can cause problems when attempting to detect damage. …
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The effects of computer-mediated communication on inductive learning by groups
… as compared to face-to-face discussion, on inductive learning by groups. An experiment was conducted in which four-person groups attempted to induce a rule which partitioned a deck of playing cards into exemplars and nonexemplars. All data for inducing the rule were presented via computer in …
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On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations
HCL achieves two functionalities: (1) flexibility in the representation, by increasing the complexity of the hypothesis comprised at each hierarchical layer, and (2) adaptability in the search for different representations, to know when to stop adding more layers in top of the hierarchical …
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Knowledge-based learning: Integration of deductive and inductive learning for knowledge base completion
… knowledge that is available. Explanation-based learning and similarity-based learning operate over a domain theory and a set of examples, respectively, but neither approach makes extensive use of both forms of knowledge. Many problems in engineering and other areas can provide a learning system …
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The impact of measurement scale on classification performance of inductive learning and statistical approaches
This thesis is a comparative study of inductive learning and statistical methods. The focus of this study is to investigate the impact of measurement scale of explanatory variables on the relative performance of the statistical method (probit) and the inductive learning method (ID3). In addition, …
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Inductive classifier learning from data: An extended Bayesian belief function approach
… reasoning under uncertainty. This thesis views inductive learning as reasoning under uncertainty and develops an Extended Bayesian Belief Function approach that allows a two-layer representation of the probabilistic rules: basic probabilistic belief and their confidences, which are independent …
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The production and control of functional electrical stimulation swing-through gait
… for controlling FES gait; and the use of machine-learning techniques. Trained, non-impaired subjects wearing adjustable braces are used to model the movement patterns of FES swing-through gait. It is found that flexing the knees during the body-swing phase of swing-through gait reduces the energy …
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Applying machine learning to the design of decision support systems for intelligent manufacturing
… presents a Decision Support System (DSS) with inductive learning capability for model management. Simulation is used as the primary environment for modeling manufacturing systems and their processes. We propose an adaptive DSS framework for incorporating machine learning into the real time …
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Knowledge-based decision support system for scheduling in a flexible flow system
… thesis, incorporating simulation modeling and inductive learning, to improve the overall performance of the system.
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Explicit Grammar Instruction and the Acquisition of Second Language Verbal Morphology: A Framework for Generalized Learning in Second Language Acquisition
… between competence-based and generalized learning processes. Within this framework, competence-based learning is theorized to employ inductive learning processes to acquire a basic set of skills designed to solve an evolutionarily static set of problems, while generalized learning is …
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Opportunistic constructive induction: Using fragments of domain knowledge to guide construction
One subfield of machine learning is the induction of a representation of a concept from positive and negative examples of the concept. Given a set of training examples, the goal of the inductive system is to create a description capable of classifying the training examples, yet general enough to …
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Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-Up Learning, and to Deduction
… forms of anti-unification are applicable to inductive logic programming (inductive learning of logic programs), speed-up learning, and knowledge base vivification (an approach to efficient deduction).
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A statistical learning framework for data mining of large-scale systems : algorithms, implementation, and applications
A machine learning framework is presented that supports data mining and statistical modeling of systems that are monitored by large-scale sensor networks. The proposed algorithm is novel in that it takes both observations and domain knowledge into consideration and provides a mechanism that …
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Learning and Reconstructing Conflicts in O-RAN
… (O-RAN). Specifically, we leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, control parameters, and Key Performance Indicators (KPIs). Our numerical results, based on a conflict model used in the Open Radio Access Network (O-RAN) …
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Expert systems in A.C. induction motor fault diagnosis.
… motor electromechanical faults. A scheme for inductive learning of new information from case histories is also reported. Based on the ID3 learning algorithm, the approach determines emerging patterns and relationships as more diagnoses are made. A method of quantifying the generality of the …
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Time Series Learning With Probabilistic Network Composites
… over time through integrated, multi-strategy learning. Its focus is on decomposable, concept learning problems for classification of spatiotemporal sequences. Systematic methods of task decomposition using attribute- driven methods, especially attribute partitioning, are investigated. This …
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