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Proceedings of the 28th IEEE Conference on Decision and Control, 2003
A rule-based system for optimizing combustion in multiple-burner installations was built and tested on the furnace of a continuous annealing line for rolled steel. The furnace has only one firing level, and the rules were elicited from energy experts with this problem in mind.
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A rule-based system for optimizing combustion in multiple-burner installations was built and tested on the furnace of a continuous annealing line for rolled steel. The furnace has only one firing level, and the rules were elicited from energy experts with this problem in mind.
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Adaptive Parsons Problems with Discourse Rules
Proceedings of the eleventh annual International Conference on International Computing Education Research, 2014Parsons problems are code segments that the user must put in the correct order with the correct indention. Research on Parsons problems suggests that they might be a more effective and efficient learning approach than writing equivalent code, especially for time-strapped secondary teachers. I intend to explore this hypothesis with empirical experiments,
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Adaptive Aspirations and Attention Rules
Academy of Management Proceedings, 2018The behavioral theory of the firm proposes that discrepancy between performance and aspirations (i.e. prior, historical or social aspirations) drives organisations’ adaptive behavior.
Luca Berchicci, Murat Tarakci
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Memory-adaptive association rules mining
Information Systems, 2004New application areas resulted in an increase of the diversity of the workloads that Data Base Management Systems have to confront. Resource management for mixed workloads is attained with the prioritization of their tasks, which during their execution may be forced to release some of their resources.
Alexandros Nanopoulos +1 more
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Adaptive Rule Adaptation in Unstructured and Dynamic Environments
2019Rule-based systems have been used to augment machine learning based algorithms for annotating data in unstructured and dynamic environments. Rules can alleviate many of shortcomings inherent in pure algorithmic approaches. Rule adaptation is a challenging and error-prone task: in a rule-based system, there is a need for an analyst to adapt rules in ...
Alireza Tabebordbar +3 more
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On Combining Case Adaptation Rules
2019The case adaptation process in case-based reasoning is often modeled as having two steps: enumerating differences between a new problem and the problem part of a retrieved case and then applying an adaptation rule for each difference. This model is sufficient when (1) predefined adaptation rules exist for all differences the system encounters, and (2 ...
David Leake, Xiaomeng Ye
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NIH Adapts Grant Resubmission Rule
Chemical & Engineering News Archive, 2014The National Institutes of Health will allow scientists whose research grants have been rejected to resubmit funding requests a second time, the agency announced earlier this month. Applicants who have rewritten their research grants once (stage A1) and had them rejected will be able to submit essentially the same application a second time.
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Stopping rules in Bayesian adaptive threshold estimation
Spatial Vision, 2005Threshold estimation with sequential procedures is justifiable on the surmise that the index used in the so-called dynamic stopping rule has diagnostic value for identifying when an accurate estimate has been obtained. The performance of five types of Bayesian sequential procedure was compared here to that of an analogous fixed-length procedure ...
Rocío, Alcalá-Quintana +1 more
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On Retention of Adaptation Rules
2014The difficulty of acquiring case adaptation knowledge is a classic problem for case-based reasoning (CBR). One method for addressing this problem is to use the cases in the case base as data from which to learn adaptation rules. For numeric prediction tasks, adaptation rules have been successfully learned from the case base by using the case difference
Vahid Jalali, David Leake
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Adaptive Ho-Kashyap rules for perceptron training
IEEE Transactions on Neural Networks, 1992Three adaptive versions of the Ho-Kashyap perceptron training algorithm are derived based on gradient descent strategies. These adaptive Ho-Kashyap (AHK) training rules are comparable in their complexity to the LMS and perceptron training rules and are capable of adaptively forming linear discriminant surfaces that guarantee linear separability and of ...
M H, Hassoun, J, Song
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