Search results for "computer.software_genre"
showing 10 items of 3858 documents
A Metamodeling Approach to Evolution
2001
With the increasing complexity of systems being modeled, analysis & design move towards more and more abstract methodologies. Most of them rely on metamodeling tools that employ multi-view models and the four-layer metamodeling architecture. Our idea is to use the metamodeling approach to classify and to constraint the possible evolutions of an information system with the effect to improve both detection of evolution conflicts and disciplined reuse. Within the domain of UML metamodeling, a refinement of the metamodel-level classification is proposed that includes bases for defining a metric of the evolution (in terms of distance between metamodels).
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…
Online Estimation of Discrete Densities
2013
We address the problem of estimating a discrete joint density online, that is, the algorithm is only provided the current example and its current estimate. The proposed online estimator of discrete densities, EDDO (Estimation of Discrete Densities Online), uses classifier chains to model dependencies among features. Each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of t…
Handling local concept drift with dynamic integration of classifiers : domain of antibiotic resistance in nosocomial infections
2006
In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at t…
Concept Maps for Comprehension and Navigation of Hypertexts
2013
Comprehension and learning with hypertexts are challenging due to the nonlinearity of such digital documents. Processing hypertexts may involve navigation and comprehension problems, leading learners to cognitive overhead. Concept maps have been added to hypertexts to reduce the cognitive requirements of navigation and comprehension. This chapter explores the literature to examine the effects of concept maps on navigation, comprehension, and learning from hypertexts. The literature review aims to elucidate how concept maps may contribute to processing hypertexts and under which conditions. In spite of the variability of concept maps used in hypertexts, some findings converge. Concept maps r…
Concordance Analysis
2011
Background In this article, we describe qualitative and quantitative methods for assessing the degree of agreement (concordance) between two measuring or rating techniques. An assessment of concordance is particularly important when a new measuring technique is introduced.
Arabic Named Entity Recognition: A Feature-Driven Study
2009
The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact …
Development and analysis of the Soil Water Infiltration Global database
2018
27 Pags.- 11 Tabls.- 8 Figs. © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.
Influence of voxel size on the accuracy of linear measurements of the condyle in images of cone beam computed tomography: A pilot study
2018
Background To analyze the influence of voxel size and exposure time on the accuracy of linear measurements of the condyle. Material and methods Four macerated hemi-mandibles of pigs were scanned in nine different voxel size protocols. Three-dimensional models of the condyle were generated in order to establish a comparison between linear measurements obtained with each voxel protocol and those obtained with a caliper (gold standard). The comparison between the protocols was performed considering the average of the two measurements of the condyle in the latero-medial (LM) and antero-posterior (AP) axes and also through repeated measurement ANOVA with rank transformation. The level of signifi…
On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases
2011
Published version of an article in the journal: Pattern Analysis and Applications. Also available from the publisher at: http://dx.doi.org/10.1007/s10044-011-0199-9 We consider the micro-aggregation problem which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing micro-aggregation techniques (MATs), it is expedient to incorporate information about the dependence between the random variable…