1,720,989 research outputs found
Belief-evidence fusion in a hybrid intelligent system
No full textA hybrid intelligent system that is able to successively refine knowledge stored in its rulebase is developed. The existing knowledge (referred to as belief rules), which may initially be defined by experts in a particular domain, is stored in the form of rules in the rulebase and is refined by comparing it with new knowledge (referred to as evidence rules) extracted from data sets trained under a neural network. Based on measurement, assessment, and interpretation of rule similarity, belief rules existing in the rulebase may be found to be confirmed, contradicted, or left unsupported by new training data. New evidence rules may also be discovered from the training data set. This rule comparison is unique in the sense that rules are viewed and compared in a geometric manner. As rules evolve in existence in the rulebase during the belief-evidence fusion process, their bounds, strengths, and certainties are also revised. The hybrid intelligent system is tested with different data sets, including hypothetical data sets and actual data sets
Assessing self-organization using order metrics
No full textProceedings of the International Joint Conference on Neural Networks6159-16485OF
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Using sample selection to improve accuracy and simplicity of rules extracted from neural networks for credit scoring applications
No full textIn this paper, we present an approach for sample selection using an ensemble of neural networks for credit scoring. The ensemble determines samples that can be considered outliers by checking the classification accuracy of the neural networks on the original training data samples. Those samples that are consistently misclassified by the neural networks in the ensemble are removed from the training dataset. The remaining data samples are then used to train and prune another neural network for rule extraction. Our experimental results on publicly available benchmark credit scoring datasets show that by eliminating the outliers, we obtain neural networks with higher predictive accuracy and simpler in structure compared to the networks that are trained with the original dataset. A rule extraction algorithm is applied to generate comprehensible rules from the neural networks. The extracted rules are more concise than the rules generated from networks that have been trained using the original datasets. © 2015 Imperial College Press
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Generalized associative memory models: Their memory capacities and potential application
No full textThe Hopfield and bi-directional associative memory (BAM) models are well developed and carefully studied models for associative memory that are patterned after the memory structure of the animal brain. Their basic limitation is that they can only perform associations between at most two sets of patterns. Several different models for generalized associative memory are proposed. These models are all extensions or generalization of the Hopfield and BAM models that can perform multiple associations. Extensive software simulations are conducted to evaluate the different models, using the memory capacity as basis for comparing their performance. Lastly, potential application of these models as data fusion systems is explored
2-D order of self-organizing kristal maps
No full textThis paper presents two metrics that measure the disorder of 2D self-organizing maps. These are the average direct neighbor distance and the average unit disorder. This theoretical work on the order of 2D self-organizing maps is done on Kristal maps, a variant of the original Kohonen model. It is shown that Kristal maps, when adequately trained, produce orderings that are superior to any of the known 2D orderings, such as the Canter-diagonal, Morton, Peano-Hilbert, raster-scan, row-prime, spiral, and random orderings
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