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2009 International Forum on Computer Science-Technology and Applications, 2009
In this paper we develop a new post-pruning algorithm. This new pruning algorithm uses two or more post-pruning algorithms to prune a decision tree that has been built on training set by different orders, and the “best” tree is selected based either on separate test set accuracy or cross-validations from trees coming from result of the above step.
Du Xiangran, Wang Xizhao, Wan Yuanyuan
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In this paper we develop a new post-pruning algorithm. This new pruning algorithm uses two or more post-pruning algorithms to prune a decision tree that has been built on training set by different orders, and the “best” tree is selected based either on separate test set accuracy or cross-validations from trees coming from result of the above step.
Du Xiangran, Wang Xizhao, Wan Yuanyuan
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Journal of Optimization Theory and Applications, 1994
The optimal, mean-square estimate of the state of a hybrid system is difficult to determine because the equations of state evolution are nonlinear and non-Gaussian. When there is a direct, albeit noisy, measurement of the modal state, it is possible to derive a useful approximation to the optimal estimator.
Sworder, D. D., Vojak, R.
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The optimal, mean-square estimate of the state of a hybrid system is difficult to determine because the equations of state evolution are nonlinear and non-Gaussian. When there is a direct, albeit noisy, measurement of the modal state, it is possible to derive a useful approximation to the optimal estimator.
Sworder, D. D., Vojak, R.
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2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
The paper presents a new graph based clustering algorithm. Traditional clustering algorithms have the drawback that it takes large number of iterations in order to come up with the desired number of clusters. The advantage of this approach is that the size of the dataset is reduced using graph based clustering approach and the required number of ...
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The paper presents a new graph based clustering algorithm. Traditional clustering algorithms have the drawback that it takes large number of iterations in order to come up with the desired number of clusters. The advantage of this approach is that the size of the dataset is reduced using graph based clustering approach and the required number of ...
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Hybrid Curve Point Distribution Algorithms
SIAM Journal on Scientific Computing, 2002Summary: Effective discretization of parametric curves has many applications in computational fields, ranging from evaluation and representation of complex geometries to geometric processing, grid generation, and numerical simulation. This paper presents a study of methods for point distribution on curves.
Khamayseh, Ahmed, Kuprat, Andrew
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2015 Open Conference of Electrical, Electronic and Information Sciences (eStream), 2015
In many research fields we have to deal with multidimensional data. Multidimensional data should be provided in a form that researcher could understand the structure of the given data. Graphical data visualization could be used for this purpose. One of the methods of graphical data visualization is multidimensional scaling.
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In many research fields we have to deal with multidimensional data. Multidimensional data should be provided in a form that researcher could understand the structure of the given data. Graphical data visualization could be used for this purpose. One of the methods of graphical data visualization is multidimensional scaling.
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Hybrid coevolutionary algorithms vs. SVM algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007As a learning method support vector machine is regarded as one of the best classifiers with a strong mathematical foundation. On the other hand, evolutionary computational technique is characterized as a soft computing learning method with its roots in the theory of evolution.
Rui Li, Bir Bhanu, Krzysztof Krawiec
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2017
In this chapter we will introduce hybrid simulation strategies as a combination of previously introduced simulation algorithms. Hybrid simulation combines the advantages of complementary simulation approaches: a system is partitioned into subsystems that are simulated with different methods.
Luca Marchetti +2 more
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In this chapter we will introduce hybrid simulation strategies as a combination of previously introduced simulation algorithms. Hybrid simulation combines the advantages of complementary simulation approaches: a system is partitioned into subsystems that are simulated with different methods.
Luca Marchetti +2 more
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2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2019
A hybrid data mining algorithm is presented in this paper. This hybridization is considered the neural network and genetic algorithm. Academic information contains the finite hidden information. This hidden information can be useful for the further planning in academics.
Anil Kumar Tiwari +3 more
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A hybrid data mining algorithm is presented in this paper. This hybridization is considered the neural network and genetic algorithm. Academic information contains the finite hidden information. This hidden information can be useful for the further planning in academics.
Anil Kumar Tiwari +3 more
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Hybrid Cloud Placement Algorithm
2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems, 2014A fully functional hybrid cloud solution requires a placement service to automatically decide whether an application should be deployed on premise, in a public cloud, or across private and public clouds. Such a service must consider application structure and communication patterns, application affinity requirements, which usually result from data ...
Merve Unuvar +2 more
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Simplified hybrid fireworks algorithm
Knowledge-Based Systems, 2019Abstract As a relatively new swarm intelligence algorithm, the fireworks algorithm (FWA) has been applied to solve lots of real-world optimization problem. However, there are still some shortcomings in the FWA algorithms. The search equation of FWA is relatively simple.
Yonggang Chen +5 more
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