Relevant bibliographies by topics / Ensembles

Academic literature on the topic 'Ensembles'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Ensembles"

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Sabzevari, Maryam, Gonzalo Martínez-Muñoz, and Alberto Suárez. "Building heterogeneous ensembles by pooling homogeneous ensembles." International Journal of Machine Learning and Cybernetics 13, no. 2 (October 13, 2021): 551–58. http://dx.doi.org/10.1007/s13042-021-01442-1.

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AbstractHeterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial and could be superior to homogeneous ensembles. In this paper, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. The proposed heterogeneous ensemble building strategy, composed of neural networks, SVMs, and random trees (i.e. from a standard random forest), is analyzed in a comprehensive empirical analysis and compared to a benchmark of other heterogeneous and homogeneous ensembles. The achieved results illustrate the gains that can be achieved by the proposed ensemble creation method with respect to both homogeneous ensembles and to the tested heterogeneous building strategy at a fraction of the training cost.
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Kieburg, Mario. "Additive matrix convolutions of Pólya ensembles and polynomial ensembles." Random Matrices: Theory and Applications 09, no. 04 (November 8, 2019): 2150002. http://dx.doi.org/10.1142/s2010326321500027.

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Recently, subclasses of polynomial ensembles for additive and multiplicative matrix convolutions were identified which were called Pólya ensembles (or polynomial ensembles of derivative type). Those ensembles are closed under the respective convolutions and, thus, build a semi-group when adding by hand a unit element. They even have a semi-group action on the polynomial ensembles. Moreover, in several works transformations of the bi-orthogonal functions and kernels of a given polynomial ensemble were derived when performing an additive or multiplicative matrix convolution with particular Pólya ensembles. For the multiplicative matrix convolution on the complex square matrices the transformations were even done for general Pólya ensembles. In the present work, we generalize these results to the additive convolution on Hermitian matrices, on Hermitian anti-symmetric matrices, on Hermitian anti-self-dual matrices and on rectangular complex matrices. For this purpose, we derive the bi-orthogonal functions and the corresponding kernel for a general Pólya ensemble which was not done before. With the help of these results, we find transformation formulas for the convolution with a fixed matrix or a random matrix drawn from a general polynomial ensemble. As an example, we consider Pólya ensembles with an associated weight which is a Pólya frequency function of infinite order. But we also explicitly evaluate the Gaussian unitary ensemble as well as the complex Laguerre (aka Wishart, Ginibre or chiral Gaussian unitary) ensemble. All results hold for finite matrix dimension. Furthermore, we derive a recursive relation between Toeplitz determinants which appears as a by-product of our results.
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Zubarev, V. Yu, B. V. Ponomarenko, E. G. Shanin, and A. G. Vostretsov. "Formation of Minimax Ensembles of Aperiodic Gold Codes." Journal of the Russian Universities. Radioelectronics 23, no. 2 (April 28, 2020): 26–37. http://dx.doi.org/10.32603/1993-8985-2020-23-2-26-37.

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Introduction. Signals constructed on the basis of ensembles of code sequences are widely used in digital communication systems. During development of such systems, the most attention is paid to analysis, synthesis and implementation of periodic signal ensembles. Theoretic methods for synthesis of periodic signal ensembles are developed and are in use. Considerably fewer results are received regarding construction of aperiodic signal ensembles with given properties. Theoretical methods for synthesis of such ensembles are practically nonexistent.Aim. To construct aperiodic Gold code ensembles with the best ratios of code length to ensemble volume among the most known binary codes.Materials and methods. Methods of directed search and discrete choice of the best ensemble based on unconditional preference criteria are used.Results. Full and truncated aperiodic Gold code ensembles with given length and ensemble volume were constructed. Parameters and shape of auto- and mutual correlation functions were shown for a number of constructed ensembles. Comparison of the paper results with known results for periodic Gold code ensembles has been conducted regarding growth of minimax correlation function values depending on code length and ensemble volume.Conclusion. The developed algorithms, unlike the known ones, make it possible to form both complete ensembles and ensembles taking into account the limitation of their volume. In addition, the algorithms can be extended to the tasks of forming ensembles from other families, for example, assembled from code sequences belonging to different families.
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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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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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Van Peski, Roger. "Spectral distributions of periodic random matrix ensembles." Random Matrices: Theory and Applications 10, no. 01 (December 19, 2019): 2150011. http://dx.doi.org/10.1142/s2010326321500118.

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Koloğlu, Kopp and Miller compute the limiting spectral distribution of a certain class of real random matrix ensembles, known as [Formula: see text]-block circulant ensembles, and discover that it is exactly equal to the eigenvalue distribution of an [Formula: see text] Gaussian unitary ensemble. We give a simpler proof that under very general conditions which subsume the cases studied by Koloğlu–Kopp–Miller, real-symmetric ensembles with periodic diagonals always have limiting spectral distribution equal to the eigenvalue distribution of a finite Hermitian ensemble with Gaussian entries which is a ‘complex version’ of a [Formula: see text] submatrix of the ensemble. We also prove an essentially algebraic relation between certain periodic finite Hermitian ensembles with Gaussian entries, and the previous result may be seen as an asymptotic version of this for real-symmetric ensembles. The proofs show that this general correspondence between periodic random matrix ensembles and finite complex Hermitian ensembles is elementary and combinatorial in nature.
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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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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.

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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.

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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Hartono, Hartono, Opim Salim Sitompul, Tulus Tulus, Erna Budhiarti Nababan, and Darmawan Napitupulu. "Hybrid Approach Redefinition (HAR) model for optimizing hybrid ensembles in handling class imbalance: a review and research framework." MATEC Web of Conferences 197 (2018): 03003. http://dx.doi.org/10.1051/matecconf/201819703003.

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The purpose of this research is to develop a research framework to optimize the results of hybrid ensembles in handling class imbalance issues. The imbalance class is a state in which the classification results give the number of instances in a class much larger than the number of instances in the other class. In machine learning, this problem can reduce the prediction accuracy and also reduce the quality of the resulting decisions. One of the most popular methods of dealing with class imbalance is the method of ensemble learning. Hybrid Ensembles is an ensemble learning method approach that combines the use of bagging and boosting. Optimization of Hybrid Ensembles is done with the intent to reduce the number of classifier and also obtain better data diversity. Based on an iterative methodology, we review, analyze, and synthesize the current state of the literature and propose a completely new research framework for optimizing Hybrid Ensembles. In doing so, we propose a new taxonomy in ensemble learning that yields a new approach of sampling-based Ensembles and will propose an optimization Hybrid Ensembles using Hybrid Approach Redefinition (HAR) Model that combines the use of Hybrid Ensembles and Sampling Based Ensembles methods. We further provide an empirical analysis of the reviewed literature and emphasize the benefits that can be achieved by optimizing Hybrid Ensembles.
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Dissertations / Theses on the topic "Ensembles"

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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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Benkaddour, Saïd. "Relation entre ensembles totalement flous et ensembles ordonnés." Lyon 1, 1986. http://www.theses.fr/1986LYO11714.

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Le present travail fait suite aux recherches menees sur la theorie des ensembles flous. D. Ponasse a defini la categorie jtf des j-ensembles totalement flous. Dans sa these de 3eme cycle g. Mycek a demontre que cette categorie est un topos lorsque j est un anti-ordinal (c. A. D. : j est un ordinal lorsqu'il est muni de l'ordre inverse). Il a exhibe tous les objets elementaires de ce topos. Wu tao, lui, a fait une etude detaillee de ce topos avec j antiordinal. Dans leurs articles j. Coulon et j. L. Coulon montrent que pour j un treillis de heyting complet la categorie jtf est equivalente a la categorie jtf et que jtf n'est pas un topos lorsque j n'est pas un anti-ordinal. Dans la premiere partie de ce travail je continue l'etude de la categorie jtf. Je demontre qu'elle est isomorphe a la categorie notee jid dont les objets sont des ensembles ordonnes et les morphismes sont des applications verifiant certaines conditions. J'ai traduit les notions de mono, epi et iso (morphisme) dans jid en notions de surjection, injection et bijection. Dans la deuxieme partie j'etudie les proprietes categoriques de jid: objet final (resp. Initial), produit (resp. Coproduit), pulback (resp. Pushout), noyau de paire (resp. Conoyau) et l'exponentielle. 1**(o)) je demontre que la plus grande famille de monomorphismes qu'on peut classer c'est la famille des monomorphismes dits forts. 2**(o)) je prouve que dans le cas ou j est un anti-ordinal tout monomorphisme est fort donc jid est un topos. 3**(o)) lorsque j n'est pas un anti-ordinal il existe des monomorphismes non forts donc non classifiables. Donc jid n'est pas un topos.
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Benkaddour, Saïd. "Relation entre les ensembles totalement flous et ensembles ordonnés." Grenoble 2 : ANRT, 1986. http://catalogue.bnf.fr/ark:/12148/cb37595915s.

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Andrle, Miroslav. "Ensembles modèles et analyse en ondelettes adaptées." Paris 7, 2002. http://www.theses.fr/2002PA077202.

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Khatib, Souad El. "Espaces métriques dans la théorie des ensembles flous." Lyon 1, 1986. http://www.theses.fr/1986LYO10060.

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Cette these est composee de trois chapitres. Le premier est un rappel des travaux de michael erceg, sur la definition d'un espace metrique flou: metric spaces in fuzzy set theory, j. M. A. , 69, 1979 (205-230). Dans cet article l'auteur a defini un ecart (une distance), flou(e) sur un ensemble flou, prenant ses valeurs dans l'ensemble des reels positif. Dans le deuxieme chapitre on introduit l'ensemble des reels flous positifs, c'est l'ensemble des applications de l'ensemble des reels dans l'intervalle unite, qui sont continues a gauche, croissantes et qui prennent la valeur zero a zero et telles la borne superieure est egale a un. Puis on decrit l'operation d'addition cet ensemble definie par sherwood et m. D. Taylor dans leur article "some pm structures on the set of distribution functions". Le troisieme chapitre constitue le but premier de cette these et consiste a donner une definition d'un quasi-ecart (ecart) flou, sur un ensemble flou, prenant ses valeurs non pas dans l'ensemble des reels positifs, mais dans l'ensemble des reels flous positifs. Puis on definit une uniformite floue sur un espace pseudometrique flou, en utilisant la definition d'une uniformite floue donnee par hutton: "uniformities on fuzzy topological spaces". En completant les resultats de hutton, on obtient des resultats sur l'engendrement des topologies floues sur les ensembles flous par des quasi-ecarts flous, on definit encore l'ecart flou conjugue, puis la distance floue
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De, Jongh Albert. "Neural network ensembles." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50035.

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Thesis (MSc)--Stellenbosch University, 2004.
ENGLISH ABSTRACT: It is possible to improve on the accuracy of a single neural network by using an ensemble of diverse and accurate networks. This thesis explores diversity in ensembles and looks at the underlying theory and mechanisms employed to generate and combine ensemble members. Bagging and boosting are studied in detail and I explain their success in terms of well-known theoretical instruments. An empirical evaluation of their performance is conducted and I compare them to a single classifier and to each other in terms of accuracy and diversity.
AFRIKAANSE OPSOMMING: Dit is moontlik om op die akkuraatheid van 'n enkele neurale netwerk te verbeter deur 'n ensemble van diverse en akkurate netwerke te gebruik. Hierdie tesis ondersoek diversiteit in ensembles, asook die meganismes waardeur lede van 'n ensemble geskep en gekombineer kan word. Die algoritmes "bagging" en "boosting" word in diepte bestudeer en hulle sukses word aan die hand van bekende teoretiese instrumente verduidelik. Die prestasie van hierdie twee algoritmes word eksperimenteel gemeet en hulle akkuraatheid en diversiteit word met 'n enkele netwerk vergelyk.
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Dromey, Christopher. "The Pierrot ensembles." Thesis, King's College London (University of London), 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.682572.

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The ‘Pierrot’ ensemble of mixed single strings, winds and piano has become standard in contemporary music, yet it is a genre that had neither been categorised nor explored analytically. This novel line-up, derived from Arnold Schoenberg’s Pierrot lunaire, Op. 21 (1912), inspired a new, British-led repertory that influenced composition as much as concert life. I chronicled this lineage of Pierrot ensembles to offer an alternative reading of twentieth-century music and culture. The thesis included analysis of salient works by Peter Maxwell Davies, Elisabeth Lutyens and Humphrey Searle, and drew on research in the Paul Sacher Stiftung (Basel) and Britten-Pears Archive (Aldeburgh), rediscovering previously unpublished works by Harrison Birtwistle and Benjamin Britten.
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Al-Razgan, Muna Saleh. "Weighted clustering ensembles." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.
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Daigle, Elise. "Examining Music Ensemble Recruitment and Retention through Student Persistence into College Performing Ensembles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523523995130136.

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Zajicek, Daniel J. "A rhetorical guide to Ebb." connect to online resource, 2006. http://www.unt.edu/etd/all/May2006/zajicek%5Fdaniel/index.htm.

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Thesis (M.M.)--University of North Texas, 2006.
For flute, oboe, clarinet, contrabassoon, horn , tuba, percussion (2 players), 2 violins, viola, cello, double bass, 1 laptop computer, and 2 or more loudspeakers. System requirements: Adobe Acrobat Reader. Duration: 10:00. Includes performance notes by the composer (p. 1-23). Includes bibliographical references (p. 23-24).
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Books on the topic "Ensembles"

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Aggarwal, Charu C., and Saket Sathe. Outlier Ensembles. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7.

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Dearling, Robert. Keyboard instruments & ensembles. Philadelphia, PA: Chelsea House Publishers, 2000.

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Dearling, Robert. Keyboard instruments & ensembles. Philadelphia: Chelsea House Publishers, 2001.

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Ma, Yo-Yo. A playlist without borders. New York, NY: Masterworks, 2013.

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

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Krivine, Jean-Louis. The orie des ensembles. Paris: Cassini, 1998.

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Hall, Michael J. W., and Marcel Reginatto. Ensembles on Configuration Space. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34166-8.

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

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Berge, Claude. Hypergraphes: Combinatoire des ensembles finis. [Paris]: Gauthier-Villars, 1987.

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Sew it yourself nursery ensembles. Montrose, Pa: Chitra Publications, 1996.

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Book chapters on the topic "Ensembles"

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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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Dorlas, Teunis C. "Ensembles." In Statistical Mechanics, 153–56. 2nd ed. Second edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003037170-28.

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Aggarwal, Charu C., and Saket Sathe. "An Introduction to Outlier Ensembles." In Outlier Ensembles, 1–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_1.

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Aggarwal, Charu C., and Saket Sathe. "Theory of Outlier Ensembles." In Outlier Ensembles, 35–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_2.

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Aggarwal, Charu C., and Saket Sathe. "Variance Reduction in Outlier Ensembles." In Outlier Ensembles, 75–161. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_3.

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Aggarwal, Charu C., and Saket Sathe. "Bias Reduction in Outlier Ensembles: The Guessing Game." In Outlier Ensembles, 163–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_4.

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Aggarwal, Charu C., and Saket Sathe. "Model Combination Methods for Outlier Ensembles." In Outlier Ensembles, 187–205. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_5.

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Aggarwal, Charu C., and Saket Sathe. "Which Outlier Detection Algorithm Should I Use?" In Outlier Ensembles, 207–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_6.

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Minlos, R. "Statistical ensembles (microcanonical and canonical ensembles, equivalence of ensembles)." In University Lecture Series, 9–14. Providence, Rhode Island: American Mathematical Society, 1999. http://dx.doi.org/10.1090/ulect/019/02.

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Henzel, Christoph. "Die Ensembles." In Die Italienische Hofoper in Berlin um 1800, 217–27. Stuttgart: J.B. Metzler, 1994. http://dx.doi.org/10.1007/978-3-476-03565-3_18.

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Conference papers on the topic "Ensembles"

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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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Mohammadi, Moslem, Hosein Alizadeh, and Behrouz Minaei-Bidgoli. "Neural Network Ensembles Using Clustering Ensemble and Genetic Algorithm." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.329.

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Luo, Xiaodong, Ibrahim Hoteit, Irene M. Moroz, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3497831.

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Agarwal, Charu. "Outlier ensembles." In the ACM SIGKDD Workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2500853.2500855.

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Silva, Anderson, Patrick Valduriez, and Fabio Porto. "Integrating Machine Learning Model Ensembles to the SAVIME Database System." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbbd_estendido.2022.21870.

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The integration of machine learning algorithms into database systems has brought new opportunities in different areas from indexing to query optimization. In this paper, we describe the integration of an approach for the automatic computation of model ensembles to answer a predictive query. We have extended the SAVIME multi-dimensional array DBMS by adding a new function to its query language and implementing the selection and allocation ensemble model dataflow into the query processing component of SAVIME. We show some initial experimental results depicting its performance against a pure Python implementation of the ensemble approach. Interestingly enough the C++ implementation within SAVIME is up to 4 times faster than its competitor.
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Balasubramanian, Vivekanandan, Antons Treikalis, Ole Weidner, and Shantenu Jha. "Ensemble Toolkit: Scalable and Flexible Execution of Ensembles of Tasks." In 2016 45th International Conference on Parallel Processing (ICPP). IEEE, 2016. http://dx.doi.org/10.1109/icpp.2016.59.

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Liu, Cong, Anli Yan, Zhenxiang Chen, Haibo Zhang, Qiben Yan, Lizhi Peng, and Chuan Zhao. "IEdroid:Detecting Malicious Android Network Behavior Using Incremental Ensemble of Ensembles." In 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2021. http://dx.doi.org/10.1109/icpads53394.2021.00104.

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Kieu, Tung, Bin Yang, Chenjuan Guo, and Christian S. Jensen. "Outlier Detection for Time Series with Recurrent Autoencoder Ensembles." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/378.

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We propose two solutions to outlier detection in time series based on recurrent autoencoder ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent neural networks (S-RNNs). Such networks make it possible to generate multiple autoencoders with different neural network connection structures. The two solutions are ensemble frameworks, specifically an independent framework and a shared framework, both of which combine multiple S-RNN based autoencoders to enable outlier detection. This ensemble-based approach aims to reduce the effects of some autoencoders being overfitted to outliers, this way improving overall detection quality. Experiments with two large real-world time series data sets, including univariate and multivariate time series, offer insight into the design properties of the proposed frameworks and demonstrate that the resulting solutions are capable of outperforming both baselines and the state-of-the-art methods.
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Li, Jinyang, Gregório R. M. da Silva, Schuyler Kain, and Selim M. Shahriar. "Control of Orientation and Entanglement of Macroscopic Magnetization Using a Single Atom." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/fio.2022.jw5a.73.

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We propose a technique that can control the macroscopic magnetization of an ensemble of atoms with a single atom. This technique can also be used for entangling two ensembles of atoms at a long distance.
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Georgiou, Ioannis T. "Intrinsic Spatio-Temporal Resolution Analysis of Nondestructive Impact Diagnostic Force Ensembles and Collocated Accelerations in a Composite Beam Structure." In ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/smasis2012-8083.

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Several ensembles of sweeping diagnostic impulsive forces were measured in a unidirectional carbon-epoxy composite beam modified locally with a soft viscoelastic patch. The spatial uniformity of the typical ensemble of diagnostic signals is addressed by a systematic spatio-temporal coherence analysis in terms of proper orthogonal decomposition (POD) modes. All samples of spanning ensembles are strongly dominated by the same POD mode characterized by a nearly uniform spatial modulation and a sharp triangular pulse time modulation. The higher POD modes have small amounts of energy. They possess an important statistics property: their spatial modulation mean value is nearly zero with standard deviation nearly identical to the nearly uniform value of the dominant POD mode. The nearly uniform spatial distribution of the dominant POD mode is a fuzzy picture of the ideal or nominal one where the impact-generated diagnostic forces should have a time waveform independent of the site of impact. Despite this energy content deficiency, the ensemble of acceleration signals acquired at a fixed point while the beam is excited by an ensemble of sweeping diagnostic forces has very robust POD modal structure. The POD modes show in a clear manner the presence of a soft viscoelastic patch simulating mass modifications. The POD-based coherence analysis of ensembles of diagnostic forces generated in this practical problem is potentially useful for a real-time verification-inspection of the integrity of networks of embedded and surface-mounted actuators and sensors.
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Reports on the topic "Ensembles"

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Dixon, Matt C. Ensembles and Their Applications. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada386942.

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Menon, Vinod P., and Charles R. Martin. Fabrication and Evaluation of Nanoelectrode Ensembles. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada293465.

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Rother, Diego, Guillermo Sapiro, and Vijay Pande. Statistical Characterization of Protein Ensembles (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada478500.

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Hansen, James A. Interpreting, Improving, and Augmenting Multi-Model Ensembles. Fort Belvoir, VA: Defense Technical Information Center, February 2006. http://dx.doi.org/10.21236/ada444387.

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Ray, Jaideep, Katherine Regina Cauthen, Sophia Lefantzi, and Lynne Burks. Conditioning multi-model ensembles for disease forecasting. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492995.

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Baldin, Ilya, Anirban Mandal, Paul Ruth, and Xin Yufeng. Orchestrating Distributed Resource Ensembles for Petascale Science. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1129195.

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Holt, Teddy R. Quantifying Uncertainty Through Global and Mesoscale Ensembles. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada531937.

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Holt, Teddy R. Quantifying Uncertainty Through Global and Mesoscale Ensembles. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada532886.

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Holt, Teddy R. Quantifying Uncertainty Through Global and Mesoscale Ensembles. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada541262.

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Hansen, James A. Interpreting, Improving, and Augmenting Multi-Model Ensembles. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada629175.

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