Relevant bibliographies by topics / Ensemble

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ensemble'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Ensemble"

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Lawrence, Andrew R., and James A. Hansen. "A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size." Monthly Weather Review 135, no. 4 (April 1, 2007): 1424–38. http://dx.doi.org/10.1175/mwr3357.1.

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Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.
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Rodríguez Molina, Javier. "Variantes léxicas y gramaticales del adverbio ensemble en la documentación medieval." Cuadernos del Instituto Historia de la Lengua, no. 7 (January 16, 2023): 405–24. http://dx.doi.org/10.58576/cilengua.vi7.129.

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En este artículo se estudia la evolución histórica del adverbiomedieval ensemble, que apenas si ha recibido atención en labibliografía. A partir de un exhaustivo análisis de documentos notariales,se defiende que ensemble no es un préstamo del francés antiguo,como sugieren varios estudios previos, sino un desarrollovernáculo del latín peninsular ĬN SĬMŬL. Esta hipótesis se basa tantoen la distribución geográfica de los datos medievales como en la variaciónmorfológica que presenta el adverbio (ensemble, ensembra,ensembla). En la Edad Media ensemble presenta un patrón de distribucióndialectal claro, ya que solo se documenta en León y en Aragón,pero no en Castilla y, a tenor de los datos presentados, dejó deusarse mucho más tarde de lo que se pensaba, pues no desaparece enel siglo XIII, sino en el XVI.
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Fiskvik, Anne Margrete. "La Famille Dansant. Investigating the Family Structure and Repertory of the Johannesénske Balletselskab." Nordic Theatre Studies 27, no. 2 (August 30, 2015): 104. http://dx.doi.org/10.7146/nts.v27i2.24254.

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The performance history of the Johannesénske Balletselskab spans a long period. In different shapes, sizes and names the ensemble was on the road for 30 years. This article analyses the activities of the Johannesénske enterprise through the lenses of itinerant performance traditions. Two features are discussed in this article: the reliance on family members as performers and the ensemble’s diverse repertory. The ensemble featured a repertory popular in its own time, consisting chiefly of national and character dances as well as pantomimes. Arguably, an investigation of the ensemble’s performance history offers information on little explored perspectives of nineteenth century Nordic ballet. In particular, the Johannesénske Balletselskab offers insights into family structures and the repertoire typical of itinerant ensembles. This information is also useful on a more general level, given that there were several ensembles similar to the Johannesénske travelling in the Nordic countries that functioned similarly to La famille dansant. Through analysing the use of the repertoire, it can be shown that the Johannesénske enterprise was, in their programming, up to date as a “ballet ensemble”. The family kept up itinerant traditions through their use of children as performers and reliance on pantomimes. The Johannesénske ensemble is, therefore, especially valuable for revealing insights into dance practices and dance styles, which have, so far, been little researched.
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Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.

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An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
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Liu, Li Min, and Xiao Ping Fan. "A Survey: Clustering Ensemble Selection." Advanced Materials Research 403-408 (November 2011): 2760–63. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2760.

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Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised classification area,it has been known that selective classifier ensembles can always achieve better solutions.Following the selective classifier ensembles,the question of clustering ensemble is defined as clustering ensemble selection.The paper introduces the concept of clustering ensemble selection and gives the survey of clustering ensemble selection algorithms.
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Kolczynski, Walter C., David R. Stauffer, Sue Ellen Haupt, Naomi S. Altman, and Aijun Deng. "Investigation of Ensemble Variance as a Measure of True Forecast Variance." Monthly Weather Review 139, no. 12 (December 1, 2011): 3954–63. http://dx.doi.org/10.1175/mwr-d-10-05081.1.

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Abstract The uncertainty in meteorological predictions is of interest for applications ranging from economic to recreational to public safety. One common method to estimate uncertainty is by using meteorological ensembles. These ensembles provide an easily quantifiable measure of the uncertainty in the forecast in the form of the ensemble variance. However, ensemble variance may not accurately reflect the actual uncertainty, so any measure of uncertainty derived from the ensemble should be calibrated to provide a more reliable estimate of the actual uncertainty in the forecast. A previous study introduced the linear variance calibration (LVC) as a simple method to determine the ensemble variance to error variance relationship and demonstrated this technique on real ensemble data. The LVC parameters, the slopes, and y intercepts, however, are generally different from the ideal values. This current study uses a stochastic model to examine the LVC in a controlled setting. The stochastic model is capable of simulating underdispersive and overdispersive ensembles as well as perfectly reliable ensembles. Because the underlying relationship is specified, LVC results can be compared to theoretical values of the slope and y intercept. Results indicate that all types of ensembles produce calibration slopes that are smaller than their theoretical values for ensemble sizes less than several hundred members, with corresponding y intercepts greater than their theoretical values. This indicates that all ensembles, even otherwise perfect ensembles, should be calibrated if the ensemble size is less than several hundred. In addition, it is shown that an adjustment factor can be computed for inadequate ensemble size. This adjustment factor is independent of the stochastic model and is applicable to any linear regression of error variance on ensemble variance. When applied to experiments using the stochastic model, the adjustment produces LVC parameters near their theoretical values for all ensemble sizes. Although the adjustment is unnecessary when applying LVC, it allows for a more accurate assessment of the reliability of ensembles, and a fair comparison of the reliability for differently sized ensembles.
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Kioutsioukis, I., and S. Galmarini. "<i>De praeceptis ferendis</i>: good practice in multi-model ensembles." Atmospheric Chemistry and Physics 14, no. 21 (November 11, 2014): 11791–815. http://dx.doi.org/10.5194/acp-14-11791-2014.

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Abstract. Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. Theoretical aspects like the bias–variance–covariance decomposition and the accuracy–diversity decomposition are linked together and support the importance of creating ensemble that incorporates both these elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi-model ensembles. The sophisticated ensemble averaging techniques, following proper training, were shown to have higher skill across all distribution bins compared to solely ensemble averaging forecasts.
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Alazba, Amal, and Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles." Applied Sciences 12, no. 9 (April 30, 2022): 4577. http://dx.doi.org/10.3390/app12094577.

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Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.
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Louk, Maya Hilda Lestari, and Bayu Adhi Tama. "Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks." Big Data and Cognitive Computing 5, no. 4 (December 6, 2021): 72. http://dx.doi.org/10.3390/bdcc5040072.

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Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issue. The purpose of this article is to address a gap in the literature by illustrating the benefits of ensemble-based models for identifying threats and attacks in a cyber-physical power grid. We provide a framework that compares nine cost-sensitive individual and ensemble models designed specifically for handling imbalanced data, including cost-sensitive C4.5, roughly balanced bagging, random oversampling bagging, random undersampling bagging, synthetic minority oversampling bagging, random undersampling boosting, synthetic minority oversampling boosting, AdaC2, and EasyEnsemble. Each ensemble’s performance is tested against a range of benchmarked power system datasets utilizing balanced accuracy, Kappa statistics, and AUC metrics. Our findings demonstrate that EasyEnsemble outperformed significantly in comparison to its rivals across the board. Furthermore, undersampling and oversampling strategies were effective in a boosting-based ensemble but not in a bagging-based ensemble.
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LaRow, T. E., S. D. Cocke, and D. W. Shin. "Multiconvective Parameterizations as a Multimodel Proxy for Seasonal Climate Studies." Journal of Climate 18, no. 15 (August 1, 2005): 2963–78. http://dx.doi.org/10.1175/jcli3448.1.

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Abstract A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying the initial conditions.
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Dissertations / Theses on the topic "Ensemble"

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Abbasian, Houman. "Inner Ensembles: Using Ensemble Methods in Learning Step." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31127.

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A pivotal moment in machine learning research was the creation of an important new research area, known as Ensemble Learning. In this work, we argue that ensembles are a very general concept, and though they have been widely used, they can be applied in more situations than they have been to date. Rather than using them only to combine the output of an algorithm, we can apply them to decisions made inside the algorithm itself, during the learning step. We call this approach Inner Ensembles. The motivation to develop Inner Ensembles was the opportunity to produce models with the similar advantages as regular ensembles, accuracy and stability for example, plus additional advantages such as comprehensibility, simplicity, rapid classification and small memory footprint. The main contribution of this work is to demonstrate how broadly this idea can be applied, and highlight its potential impact on all types of algorithms. To support our claim, we first provide a general guideline for applying Inner Ensembles to different algorithms. Then, using this framework, we apply them to two categories of learning methods: supervised and un-supervised. For the former we chose Bayesian network, and for the latter K-Means clustering. Our results show that 1) the overall performance of Inner Ensembles is significantly better than the original methods, and 2) Inner Ensembles provide similar performance improvements as regular ensembles.
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Wild, Florence. "ENSEMBLE PIECE." Thesis, Konstfack, Institutionen för Konst (K), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:konstfack:diva-5774.

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The essay Ensemble Piece contemplates the nature of how one observes, labours, and translates as active and passive movements, repeated, reconstructed. The text is a companion piece to the installations Silver Tea and Silver Tea Suite. The works discussed in this text make up the installation Silver Tea. A contemplation of forms that kaleidoscopically revolve around each other, whose imagery, methodologies and forms reflect and morph into one another, a looping, repetitious rhythm that is not, however, confined to a circuitous nature. Silver Tea rests in a space and timeframe between suggestion, intention, and intuition, where gestures and their traces have taken on a life of their own.   Silver Tea, a literal translation of the Swedish word silverte, is a misleading phrase. Silverte is, in its essence, simply a cup of hot water, therefore being neither silver, nor in fact, tea. But sometimes language can imbue in an object a sensation more magical than the sum of its parts. Later, thinking back on my early years in Sweden...
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Yang, Shuang. "Multistage neural network ensemble : adaptive combination of ensemble results." Thesis, London Metropolitan University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425920.

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Anderson, Andrew E. (Andrew Edwin). "Hailstones and Birdcages for Wind Ensemble." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc500256/.

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Hailstones and Birdcages is a composition of approximately thirteen minutes' duration and is scored for two flutes and piccolo, two oboes and english horn, three Eb clarinets, E clarinete, bass clarinet, two bassoons, two Eb alto saxophones, Bb tenor saxophone, three Bb trumpets, four F horns, three trombones, euphonium, two tubas, and three percussionists. Four instruments--one each of flute, oboe, Bb clarinet, and trombone--are used in concertante like fashion, and there are prominent solo passages for the first bassoon, as well. The work is a single movement in three sections, fast - slow - fast, with ritornello. and employs a free use of the total chromatic. Technically, the work is within the capabilities of an above-average high school or average college wind ensemble
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McDonald, Richard F. (Richard Frederic). "Kinetico for Chamber Wind Ensemble." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc504015/.

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This single movement work is written for 2 flutes, 2 oboes, 3 clarinets in Bb, bass clarinet in Bb, 2 bassoons, alto saxophone in Eb, 2 horns in F, 2 trumpets in Bb, trombone, euphonium, tuba, contra bass, and 3 percussion. The approximate length is eight minutes. Both traditional and proportional systems of notation are employed. The entire piece is freely chromatic with some implications of whole tone and other nondiatonic scales. The harmonies are tertian yet have no functional tonal basis. Changing meters with asymmetrical divisions are used in all sections except C and E, which have time indications (in seconds) for each measure with subdivisions to aid the conductor. There are seven major formal divisions: A B transition C retransition A' D E.
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Cech, Andrew J. "Brother's lament for wind ensemble." Ohio : Ohio University, 2009. http://www.ohiolink.edu/etd/view.cgi?ohiou1244578892.

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Guérin, Laura. "Manger ensemble : mourir ensemble : ethnographie du repas collectif en Ehpad." Paris, EHESS, 2016. http://www.theses.fr/2016EHES0121.

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A partir d'une démarche ethnographique, cette thèse interroge le maintien du repas collectif en Établissement d'Hébergement pom Personnes Âgées Dépendantes (Ehpad). Préconisé par les politiques de santé publique et la littérature professionnelle comme gage de soin et de sociabilité, le repas en commun n'en est pas moins une épreuve quotidienne au sein des établissements, qui impose des ajustements pratiques afin de correspondre, autant que possible, aux attentes sociales qu'il cristallise. Parce qu'il exige chaque jour la mise en groupe des résidents, concentre la vie en collectivité, nous l'examinons comme un dispositif particulier de socialisation de la vieillesse dite dépendante. Deux axes d'analyse sont alors privilégiés. D'une part, il s'agit de saisir le repas collectif en Ehpad comme le résultat d'un travail particulier : un ensemble de manières de faire et de dire qui agissent sur les résidents, et dont les visées principales sont l'incorporation alimentaire et le maintien des dispositions sociales. D'autre part, l'analyse des embarras professionnels qu'un tel travail engage, et les formes d'opposition des résidents qui lui correspondent, permettent de dépasser la seule médicalisation des comportements. Le matériel d'enquête se compose d'observation participante dans trois établissements en tant que membre stagiaire du service de restauration, à laquelle s'ajoute une analyse des recommandations de bonnes pratiques professionnelles rédigées par la Haute Autorité de Santé (HAS) et l'Agence Nationale d'Évaluation des Services sociaux et Médico-sociaux (ANESM). Grâce à une recension de littérature biomédicale, l'appréhension du décalage entre les normes et les pratiques est situé dans un espace de référence plus large, là où se construil de manière savante la prise en charge de la dénutrition. Enfin, une mise en perspective historique du repas en Ehpad, par la prise en compte du repas à l'hospice à la fin du XIXe siècle et les thèses médicales sur la nutrition au grand âge qui lui font suite, fait apparaître l'évolution du traitement de la vieillesse par les consommations. Dans son ensemble, c'est le gouvernement des corps qui est interrogé dans sa disposition à produire des espoirs quant au maintien de la vie matérielle et symbolique des individus reconnus comme vulnérables, supposés bientôt mourir
Based on an ethnographic approach, this dissertation work constitutes an inquiry into the upholding of collective meals within French nursing homes dedicated to the care of dependent elderly populations (Ehpad). Recommended by public health policies and the professional literature as a token of care and sociability, the communal meal is none the less a daily trial within establishments, which commands practical adjustments in order to match, to the extent possible, the social expectations it crystallizes. Because each day it requires residents to be gathered, concentrates community living, we examine it as a particular socializing arrangement for the dependent elderly population. Hence the two lines of analysis we have favored. Firstly, we intend to grasp the Ehpad communal meal as the result of a specific labor: a set of ways of doing and saying that affecl residents, the focal aims of which laying with incorporating food and maintaining social arrangements. Secondly, by analyzing professional embarrassment that this work involves, as well as matching forms ol oppositions on residents' part, we overcome the mere medicalization of behaviors. Inquiry materials comprise participant observation in three establishments as an intern within the restaurant services, together with an analysis of professional best practices written by the French higher health authority and the French national agency for the evaluation of social and medico-social services. Through a review of the biomedical literature, apprehending the gap between norms and practices is placed in a widen reference space, where undernourishment is being built in a scholarly fashion. At last, a historical perspective of the Ehpad meal, through considering late XlXth century hospice meals as well as resulting medical theories on great-age nutrition, brings out the evolutions of elderly's treatment through consumptions. Across this dissertation, we have inquired into the government of bodies, in its disposition to produce hopes as foi maintaining the material and symbolic life of individuals known to be vulnerable and to soon face death
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Echevarría, García Joaquín, and Barrantes James José Ordinola. "Proyecto ensemble interactions." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2016. http://hdl.handle.net/10757/622175.

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En la actualidad, no existe gran variedad de herramientas que permitan a la comunidad de desarrolladores Android implementar aplicaciones orientadas a entornos de múltiples pantallas y que, adicionalmente, les permitan cumplir con lo descrito por Ensemble Interactions y los patrones de diseño definidos para estos entornos, los cuales, en conjunto, son conceptos que proponen el trabajo en conjunto de todos los dispositivos disponibles en un mismo entorno. Por otro lado, de las pocas herramientas que permiten el desarrollo de este tipo de aplicaciones, algunas han sido implementadas por grandes empresas desarrolladoras de dispositivos móviles que limitan sus herramientas para que funcionen únicamente con los dispositivos que le pertenecen. Por todo ello, este trabajo busca implementar una herramienta que permita a la comunidad de desarrolladores Android desarrollar aplicaciones móviles orientadas a entornos de múltiples dispositivos y que se alineen a lo definido por Ensemble Interactions y los patrones de diseño. Dicha herramienta basará la interacción entre los dispositivos sobre una red Wi-Fi sin la necesidad que esta cuente con acceso a internet. Asimismo, esta herramienta estará disponible en dos versiones, una para el desarrollo de aplicaciones Android y otra para el desarrollo de aplicaciones en Samsung Smart TVs. A continuación, en el primer capítulo se expondrá el proyecto y su gestión. Luego, se dará paso al marco teórico sobre el que se basa este trabajo. En el tercer capítulo se analizará a detalle la situación actual de todas herramientas y tecnologías que permiten desarrollar aplicaciones orientadas a entornos de múltiples pantallas. A continuación, en el quinto capítulo describirá la metodología de trabajo empleada en el desarrollo de este proyecto y los resultados obtenidos. En el sexto capítulo se presentará el producto final del trabajo y las medidas de gestión tomadas a lo largo de su desarrollo. Finalmente, se listarán las conclusiones y recomendaciones propuestas con este trabajo.
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Alza, Guzmán Diego Andrés, and Martinez Jean Carlo Canales. "Ensemble interactions iOS." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2018. http://hdl.handle.net/10757/622927.

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Definir e implementar un SDK que integre las principales características definidas por Ensemble Interactions (EI). Para ello, se analizarán e implementarán los patrones descritos por E.I. De esta forma, la investigación brindará el marco teórico necesario para el correcto entendimiento de EI y los seis patrones principales que lo definen. La interpretación de los resultados obtenidos en el análisis de los patrones será fundamental debido a que esta será la base que respaldara los artefactos que se realizaran a lo largo del proyecto. Asimismo, la implementación se dividirá en entregables: Desarrollar un SDK que permita la conectividad entre los dispositivos mediante WiFi y Bluetooth, así como implementar dos pruebas de concepto que implementen los patrones descritos por EI, por último, desarrollar una aplicación final que integre las características principales de Ensemble Interactions. Con respecto a los riesgos presentes en el desarrollo del proyecto, se ha remarcado el hecho que, actualmente, no existe una aplicación que integre todos los conceptos descritos por EI en los dispositivos Apple, por lo cual plantear el desarrollo de una aplicación que lo haga es un reto significativo. Asimismo, un riesgo fundamental del proyecto es la correcta interpretación de los patrones y su implementación.
Nowadays, there are not many tools that allow IOS developer community implement applications oriented to multi-display environments and, additionally, to fulfill what is described by Ensemble Interactions and the design patterns defined for these environments, which are concepts that propose the joint work of all available devices in the same environment. On the other hand, of the few tools that allow the development of such applications, some have been implemented by large companies that also develop mobile devices and limit their tools to work with only those devices from their own. Therefore, this paper seeks to implement a tool that allows IOS developer communitydevelop mobile applications for multiple devices environments that meet what is described by Ensemble Interactions and design patterns. This tool base the interaction between devices on a Wi-Fi network without access to the internet and Bluetooth. Then, in the first chapter the project and its management will be discussed. Then, the theoretical framework on which this work is based. The third chapter will analyze in detail the current status of all tools and technologies that allow the development of multi-display environments applications. Then, the fifth chapter will describe the methodology employed in the development of this project and the results obtained. In the sixth chapter, the final product of labor and management measures taken during its development will be presented. Finally, conclusions and recommendations proposed in this work are listed.
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Andersson, Erik. "Kommunikation i ensemble." Thesis, Örebro universitet, Musikhögskolan, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-48327.

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Books on the topic "Ensemble"

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Cosier, Tony. Ensemble. St. Catharines, Ont: Moonstone Press, 1985.

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Comeau, Raymond F. Ensemble. 6th ed. Fort Worth: Holt, Rinehart and Winston, 1999.

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Comeau, Raymond F. Ensemble. 6th ed. Fort Worth: Holt, Rinehart and Winston, 1999.

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Comeau, Raymond F. Ensemble. 6th ed. Fort Worth: Holt, Rinehart and Winston, 1998.

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Comeau, Raymond F. Ensemble. 5th ed. Fort Worth: Holt, Rinehart and Winston, 1994.

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Comeau, Raymond F. Ensemble. 5th ed. Fort Worth: Holt, Rinehart and Winston, 1994.

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Comeau, Raymond F. Ensemble. 5th ed. Fort Worth: Holt, Rinehart and Winston, 1994.

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Glass, Philip. Songs from liquid days. New York, N.Y: CBS, 1986.

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McDonald, Donna. The odyssey of the Philip Jones Brass Ensemble. Bulle, Switzerland: Editions Bim, 1986.

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Brine, Patricia. Enigmatic ensemble. Edmonton: Macbee Pub., 2005.

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Book chapters on the topic "Ensemble"

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Zhou, Zhi-Hua. "Ensemble." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_768-2.

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Zhou, Zhi-Hua. "Ensemble." In Encyclopedia of Database Systems, 988–91. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_768.

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Vens, Celine. "Ensemble." In Encyclopedia of Systems Biology, 660–61. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_614.

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Zhou, Zhi-Hua. "Ensemble." In Encyclopedia of Database Systems, 1297–301. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_768.

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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning in Ensemble Methods." In Metalearning, 189–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_10.

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AbstractThis chapter discusses some approaches that exploit metalearning methods in ensemble learning. It starts by presenting a set of issues, such as the ensemble method used, which affect the process of ensemble learning and the resulting ensemble. In this chapter we discuss various lines of research that were followed. Some approaches seek an ensemble-based solution for the whole dataset, others for individual instances. Regarding the first group, we focus on metalearning in the construction, pruning and integration phase. Modeling the interdependence of models plays an important part in this process. In the second group, the dynamic selection of models is carried out for each instance. A separate section is dedicated to hierarchical ensembles and some methods used in their design. As this area involves potentially very large configuration spaces, recourse to advanced methods, including metalearning, is advantageous. It can be exploited to define the competence regions of different models and the dependencies between them.
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Resseguier, Valentin, Yicun Zhen, and Bertrand Chapron. "Constrained Random Diffeomorphisms for Data Assimilation." In Mathematics of Planet Earth, 281–92. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40094-0_13.

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AbstractFor ensemble-based data assimilation purposes, there is a definite need for relevant ensemble sampling tools. Indeed, the quality and spreading of these ensembles have deep implications in the quality of the data assimilation, and—until recently—those so-called covariance inflation tools have mostly relied on unsuitable linear Gaussian frameworks. A promising alternative is the generation of ensembles through a stochastic remapping of the physical space.
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Hennicker, Rolf, Alexander Knapp, and Martin Wirsing. "Epistemic Ensembles." In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, 110–26. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_8.

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AbstractAn ensemble consists of a set of computing entities which collaborate to reach common goals. We introduce epistemic ensembles that use shared knowledge for collaboration between agents. Collaboration is achieved by different kinds of knowledge announcements. For specifying epistemic ensemble behaviours we use formulas of dynamic logic with compound ensemble actions. Our semantics relies on an epistemic notion of ensemble transition systems as behavioural models. These transition systems describe control flow over epistemic states for expressing knowledge-based collaboration of agents. Specifications are implemented by epistemic processes that are composed in parallel to form ensemble realisations. We give a formal operational semantics of these processes that generates an epistemic ensemble transition system. A realisation is correct w. r. t. an ensemble specification if its semantics is a model of the specification.
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Greiner, Walter, Ludwig Neise, and Horst Stöcker. "Ensemble Theory and Microcanonical Ensemble." In Thermodynamics and Statistical Mechanics, 142–58. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0827-3_6.

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Blaich, Anna, Felix Grädler, Henning Mohr, and Hannes Seibold. "Ensemble Colourage." In Kultur:Wandel - Impulse für eine zukunftsweisende Kulturpraxis, 322–26. Bielefeld, Germany: transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839464922-043.

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André Uelner widmet sich in diesem Beitrag der Kooperation zwischen der Orientalischen Musikakademie Mannheim (OMM), der Deutschen Staatsphilharmonie Rheinland-Pfalz und der Popakademie Baden-Württemberg, die in der Gründung des transkulturellen Ensembles Colourage mündete. Die am Ensemble beteiligten Musiker:innen der Staatsphilharmonie und der OMM entwickeln basisdemokratisch und ohne musikalische Leitung ein eigenes musikalisches Repertoire mit einem Verständnis zwischen westlich gedachter klassischer Musik und maqambasierten Musiktraditionen aus den Regionen der Türkei sowie des Nahen- und Mittleren Ostens. Grundgedanke dieses fortlaufenden Projektes ist die gegenwartsbezogene Weitung der Musikpraxis von Orchestern und Klangkörpern hin zu einer zeitgenössischen Musik in einer postmigrantischen Gesellschaft.
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Polikar, Robi. "Ensemble Learning." In Ensemble Machine Learning, 1–34. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9326-7_1.

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Conference papers on the topic "Ensemble"

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Alabi, Oluwafemi S., Xunlei Wu, Jonathan M. Harter, Madhura Phadke, Lifford Pinto, Hannah Petersen, Steffen Bass, et al. "Comparative visualization of ensembles using ensemble surface slicing." In IS&T/SPIE Electronic Imaging, edited by Pak Chung Wong, David L. Kao, Ming C. Hao, Chaomei Chen, Robert Kosara, Mark A. Livingston, Jinah Park, and Ian Roberts. SPIE, 2012. http://dx.doi.org/10.1117/12.908288.

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Cheung, Catherine, and Zouhair Hamaimou. "Ensemble Integration Methods for Load Estimation." In Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17553.

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Helicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. To increase confidence in the load estimation process, ensemble methods are employed combining multiple individual load estimators that increase predictive stability across flights and add robustness to noisy data. In this work, several load estimation methods are applied to a variety of machine learning algorithms to build a large library of individual load estimation models for main rotor yoke loads from 28 flight state and control system parameters. This paper explores several ensemble integration methods including simple averaging, weighted averaging using rank sum, and forward selection. From the 426 individual models, 25 top models were selected based on four ranking metrics, root mean squared error (RMSE), correlation coefficient, and interquartile ranges of these two metrics. All ensembles achieved improved performance for these four metrics compared to the best individual model, with the forward selection ensemble obtaining the lowest RMSE, highest correlation, and closest load signal prediction visually of all models.
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Nemani, Venkat, Adam Thelen, Chao Hu, and Steve Daining. "Dynamically Weighted Ensemble of Diverse Learners for Remaining Useful Life Prediction." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90252.

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Abstract A traditional ensemble approach to predicting the remaining useful life (RUL) of equipment and other assets has been constructing data-driven and model-based ensembles using identical estimators. This ensemble approach may perform well on quality data collected from a design-of-experiments test but may ultimately fail when deployed in the field because of higher-than-expected noise, missing measurements, and different degradation trends. In such work environments, the high similarity of the learners can lead to large under/overestimates of RUL, where the ensemble is only as accurate as the learner which under/overestimates RUL the least. In response to this, we investigate whether an ensemble of diverse learners might be able to predict RUL consistently and accurately by aggregating the predictions of various algorithms which are found to perform differently under the same conditions. We propose improving ensemble model performance by 1) using a combination of diverse learning algorithms which are found to perform differently under the same conditions and 2) training a data-driven model to adaptively estimate the prediction weight each learner receives. The proposed methods are evaluated using open-source run-to-failure datasets from two popular systems of prognostics research: lithium-ion batteries and rolling element bearings. Results for the lithium-ion battery case study indicate that the proposed method achieves a 12% improvement in RUL prediction accuracy and a 27% decrease in the average uncertainty calibration error when compared to ensembles without dynamic weighting, and an even larger improvement when compared to individual models.
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Hislop, Gregory W., Lillian Cassel, Lois Delcambre, Edward Fox, Rick Furuta, and Peter Brusilovsky. "Ensemble." In the 10th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631728.1631783.

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Lillian (Boots), Cassel, Hislop Gregory, and Bharath Nadella. "Ensemble." In the 14th annual ACM SIGCSE conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1562877.1562997.

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Cassel, Lillian, Lois Delcambre, and Gregory Hislop. "Ensemble." In the 45th ACM technical symposium. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2538862.2544252.

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Cadot, Sidney, Frits Kuijlman, Koen Langendoen, Kees van Reeuwijk, and Henk Sips. "ENSEMBLE." In the ACM SIGPLAN workshop. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/384197.384207.

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Cadot, Sidney, Frits Kuijlman, Koen Langendoen, Kees van Reeuwijk, and Henk Sips. "ENSEMBLE." In the 2001 ACM SIGPLAN workshop. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/384198.384207.

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Xiang, P. F., C. Williams, S. X. Yang, A. T. T. Ying, P. Cheng, Y. B. Dang, K. Ehrlich, et al. "Ensemble." In the 2008 international workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1454247.1454259.

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Andersen, Kristina. "'Ensemble'." In Extended abstracts of the 2004 conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/985921.986033.

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Reports on the topic "Ensemble"

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Cobb, Kelly. Saori Ensemble. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1253.

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Chae, May. Lucky Ensemble. Ames: Iowa State University, Digital Repository, September 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1586.

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Wang, Xugueng. Ensemble Forecasting with the Ensemble Transform Kalman Filter. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada429161.

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Pulley, Zoe, and kristin caskey. Zero Waste Ensemble. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1202.

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Orzada, Belinda T. Denim and Silk Ensemble. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-235.

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Buckley, R., S. Chiswell, R. Kurzeja, G. Maze, B. Viner, and D. Werth. Advanced Atmospheric Ensemble Modeling Techniques. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1395969.

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Orzada, Belinda T. Upcycle and Zero Waste Ensemble. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-236.

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Szunyogh, Istvan. Tropical Cyclone Ensemble Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada542060.

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Peil, Steven, Scott Crane, James Hanssen, Thomas B. Swanson, and Christopher B. Ekstrom. An Ensemble of Atomic Fountains. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada566333.

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Szunyogh, Istvan. Tropical Cyclone Ensemble Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574022.

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