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The Journal of Thoracic and Cardiovascular Surgery, 2015 Some of the next articles are maybe not open access.
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Gaussian Ensemble as an Interpolating Ensemble
Physical Review Letters, 1988We consider a noncanonical ensemble which involves a sample thermally connected to a finite heat bath with specific properties. Treating the size of the heat bath as a parameter, we show that static properties of finite samples are ensemble dependent.
, Challa, , Hetherington
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From cluster ensemble to structure ensemble
Information Sciences, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhiwen Yu 0002 +3 more
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International Journal of Human-Computer Interaction, 2001
The technology space that includes ubiquitous computing, information appliances, and pervasive and situated systems is the subject of intense interest among the research community. In this article, we examine some of the principles implicit in this space and introduce the idea of “ensemble computing” to describe technologies that extend this space.
Gellersen, Hans, Thomas, Peter
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The technology space that includes ubiquitous computing, information appliances, and pervasive and situated systems is the subject of intense interest among the research community. In this article, we examine some of the principles implicit in this space and introduce the idea of “ensemble computing” to describe technologies that extend this space.
Gellersen, Hans, Thomas, Peter
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Dynamic Ensemble of Ensembles in Nonstationary Environments
2013Classifier ensemble is an active topic for learning from non-stationary data. In particular, batch growing ensemble methods present one important direction for dealing with concept drift involved in non-stationary data. However, current batch growing ensemble methods combine all the available component classifiers only, each trained independently from ...
Xu-Cheng Yin, Kaizhu Huang, Hong-Wei Hao
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Knowledge-Based Systems, 2006
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here, an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points.
Zhi-Hua Zhou, Wei Tang
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Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here, an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points.
Zhi-Hua Zhou, Wei Tang
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost.
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We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost.
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2005
Neural network ensembles are widely use for classification and regression problems as an alternative to the use of isolated networks. In many applications, ensembles has proven a performance above the performance of just one network. In this paper we present a new approach to neural network ensembles that we call “cascade ensembles”. The approach is
Nicolás García-Pedrajas +3 more
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Neural network ensembles are widely use for classification and regression problems as an alternative to the use of isolated networks. In many applications, ensembles has proven a performance above the performance of just one network. In this paper we present a new approach to neural network ensembles that we call “cascade ensembles”. The approach is
Nicolás García-Pedrajas +3 more
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WIREs Data Mining and Knowledge Discovery, 2011
AbstractCluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as theconsensussolution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints ...
Joydeep Ghosh, Ayan Acharya
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AbstractCluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as theconsensussolution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints ...
Joydeep Ghosh, Ayan Acharya
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Ensemble Theory and Microcanonical Ensemble
1995In the preceding sections we have seen how one can—at least in principle—calculate the macroscopic properties of a closed system for given E, V, and N. We now want to develop a more general formalism which we can also use to describe different situations (e.g., a system at a given temperature in a heat bath). In a given macrostate a system can assume a
Walter Greiner +2 more
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