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Institution

University of Bahrain

EducationMadīnat ‘Īsá, Bahrain
About: University of Bahrain is a education organization based out in Madīnat ‘Īsá, Bahrain. It is known for research contribution in the topics: Thin film & Doping. The organization has 1650 authors who have published 3405 publications receiving 48162 citations. The organization is also known as: Bahrain University & UoB.


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Journal ArticleDOI
TL;DR: In this article, error-correcting output codes are employed as a distributed output representation to improve the performance of decision-tree algorithms for multiclass learning problems, such as C4.5 and CART.
Abstract: Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by studying collections of training examples of the form (xi, f(xi)). Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range of multiclass learning tasks. We also demonstrate that this approach is robust with respect to changes in the size of the training sample, the assignment of distributed representations to particular classes, and the application of overfitting avoidance techniques such as decision-tree pruning. Finally, we show that--like the other methods--the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.

2,542 citations

Posted Content
TL;DR: It is demonstrated that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.
Abstract: Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k ``classes''). The definition is acquired by studying collections of training examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range of multiclass learning tasks. We also demonstrate that this approach is robust with respect to changes in the size of the training sample, the assignment of distributed representations to particular classes, and the application of overfitting avoidance techniques such as decision-tree pruning. Finally, we show that---like the other methods---the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.

2,455 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a summary of algorithms of environmental-economic dispatch in electric power systems since 1970, which attempt to reduce the production of atmospheric emissions such as NO/sub x/ and SO/sub X/ caused by the operation of fossil-fueled thermal generation.
Abstract: Traditionally electric power systems are operated in such a way that the total fuel cost is minimized regardless of emissions produced. With increased requirements for environmental protection, alternative strategies are required. This paper presents a summary of algorithms of environmental-economic dispatch in electric power systems since 1970. The algorithms attempt to reduce the production of atmospheric emissions such as NO/sub x/ and SO/sub x/ caused by the operation of fossil-fueled thermal generation. Such reduction is achieved by including emissions either as a constraint or as a weighted function the objective of the overall dispatching problem. >

459 citations

Journal ArticleDOI
TL;DR: An adaptive fuzzy gain scheduling scheme for conventional PI and optimal load frequency controllers and a Sugeno type fuzzy inference system is used in the proposed controller.
Abstract: An adaptive fuzzy gain scheduling scheme for conventional PI and optimal load frequency controllers has been proposed. A Sugeno type fuzzy inference system is used in the proposed controller. The Sugeno type fuzzy inference system is extremely well suited to the task of smoothly interpolating linear gains across the input space when a very nonlinear system moves around in its operating space. The proposed adaptive controller requires much less training patterns than a neural net based adaptive scheme does and hence avoiding excessive training time. Results of simulation show that the proposed adaptive fuzzy controller offers better performance than fixed gain controllers at different operating conditions.

385 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202326
2022106
2021284
2020359
2019264
2018202