This paper presents an algorithm for extraction of phrases from text *** e algorithm builds phrases by iteratively merging bigrams according to an association *** o association measures are presented: mutual informati...
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This paper presents a Bayes document classifier using phrases as *** e phrases are extracted using a grammar that iteratively applies the rules to the sequence of words in the document. This grammar is generated from ...
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Cluster analysis is an un-supervised learning technique that is widely used in the process of topic discovery from text. The research presented here proposes a novel un-supervised learning approach based on aggregatio...
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In this paper, we combine a novel set of image features called virtual circles with edge direction, to provide an efficient alignment algorithm for image registration under similarity transformations. Virtual circles ...
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In this paper, we combine a novel set of image features called virtual circles with edge direction, to provide an efficient alignment algorithm for image registration under similarity transformations. Virtual circles ...
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There exist numerous schemes and methods to determine the output of an ensemble of classifiers. The most common approach being the majority vote. Furthermore, we might expect that an improvement can be achieved if the...
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There exist numerous schemes and methods to determine the output of an ensemble of classifiers. The most common approach being the majority vote. Furthermore, we might expect that an improvement can be achieved if there is a method by which we may weigh the members of the ensemble according to their individual performance. The feature based approach presented an architecture that tries to approach this target. However, if there is a way that the final classification may influence these weights we should expect an increased performance in the overall classification task. In this paper we present a new training algorithm that utilizes a feedback mechanism to iteratively improve the classification capability of the feature based approach. This approach is compared with the standard training method as well as standard aggregation schemes for combining classifier ensembles. Empirical results show that this architecture improved on classification accuracy.
A new evolutionary algorithm for the constrained multiple destination routing problem is presented. The constrained multicast problem is characterized by a minimum cost multicast tree and a bounded end-to-end delay. I...
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A new evolutionary algorithm for the constrained multiple destination routing problem is presented. The constrained multicast problem is characterized by a minimum cost multicast tree and a bounded end-to-end delay. It has been proven that this problem is NP-complete. The proposed algorithm is based on a soft computing technique that integrates in an efficient manner the merits of genetic algorithms and concepts of the competitive learning in the artificial neural networks literature. A population based learning algorithm is utilized, among other techniques, to construct a delay bounded multicast tree. A salient feature of the algorithm is the adaptive learning concept that achieves an efficient trade-off between the exploration and exploitation of the search space.
In the literature, there already exist some fuzzy approaches to edge detection. However, they are generally computationally expensive. In this paper, several fast fuzzy edge detectors are introduced for practical case...
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In the literature, there already exist some fuzzy approaches to edge detection. However, they are generally computationally expensive. In this paper, several fast fuzzy edge detectors are introduced for practical cases where a rough edge map is needed in a short time.
This paper presents a new approach to understanding mental models for complex industrial systems. Using the Abstraction Hierarchy (AH) framework as a way to describe mental models, we propose an approach to assess eco...
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This paper presents a new approach to understanding mental models for complex industrial systems. Using the Abstraction Hierarchy (AH) framework as a way to describe mental models, we propose an approach to assess ecological compatibility between an operator's internal mental model and a model of the environment. In order to do so, we have combined and built upon the previous work of Rasmussen (1979) and Moray (1996). We believe that this new approach can assess ecological compatibility and ultimately provide the operator with a more accurate and complete internal mental model that reflects the reality of the environment. We argue that Ecological interfacedesign (EID) is a way to link mental models and the environment. Future work will be needed in order to assess ecological compatibility. This includes capturing and examining operators' internal mental models, and comparing them with models of the environment.
This paper presents a new approach to understanding mental models for complex industrial systems. Using the Abstraction Hierarchy (AH) framework as a way to describe mental models, we propose an approach to assess eco...
This paper presents a new approach to understanding mental models for complex industrial systems. Using the Abstraction Hierarchy (AH) framework as a way to describe mental models, we propose an approach to assess ecological compatibility between an operator's internal mental model and a model of the environment. In order to do so, we have combined and built upon the previous work of Rasmussen (1979) and Moray (1996). We believe that this new approach can assess ecological compatibility and ultimately provide the operator with a more accurate and complete internal mental model that reflects the reality of the environment. We argue that Ecological interfacedesign (EID) is a way to link mental models and the environment. Future work will be needed in order to assess ecological compatibility. This includes capturing and examining operators' internal mental models, and comparing them with models of the environment.
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