Results 11 to 20 of about 3,977,371 (295)
A virtual workspace for hybrid multidimensional scaling algorithms [PDF]
, 2003 In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon ...
Ross, G., Chalmers, M.
core +8 more sources
Properties of Random Graphs via Boltzmann Samplers [PDF]
Discrete Mathematics & Theoretical Computer Science, 2007 This work is devoted to the understanding of properties of random graphs from graph classes with structural constraints. We propose a method that is based on the analysis of the behaviour of Boltzmann sampler algorithms, and may be used to obtain precise
Konstantinos Panagiotou, Andreas Weißl
doaj +1 more source
Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling
Entropy, 2023 Nested sampling is an efficient method for calculating Bayesian evidence in data analysis and partition functions of potential energies. It is based on an exploration using a dynamical set of sampling points that evolves to higher values of the sampled ...
Lune Maillard +2 more
doaj +1 more source
A Sampling Algorithm for Diffusion Networks [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational cost,
Daniel G. Tiglea +2 more
openaire +2 more sources
Applied Sciences, 2020 ‘El Diario de Juárez’ is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants that publishes texts of which citizens read them on both a website and an RSS (Really Simple Syndication) service.
Gilberto Rivera +4 more
doaj +1 more source
SNNTool: A software tool for sampling neural networks algorithms implementation
SoftwareX, 2023 SNNTool is an efficient utility designed for researchers and developers, providing methods for building, training, and applying various Sampling Neural Networks (SNNs). SNN is a novel neural network with high accuracy, fast convergence, low computational
Lingyan Wu, Gang Cai
doaj +1 more source
Geometric algorithms for sampling the flux space of metabolic networks
Journal of Computational Geometry, 2023 Metabolic networks and their reconstruction set a new era in the analysis of metabolic and growth functions in the various organisms. By modeling the reactions occurring inside an organism, metabolic networks provide the means to understand the ...
Apostolos Chalkis +4 more
doaj +1 more source
On sampling error in evolutionary algorithms [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021 The initial population in evolutionary algorithms (EAs) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to incorporate a sampling error.
Dirk Schweim +2 more
openaire +1 more source
Enhanced Sampling Algorithms [PDF]
, 2012 42 pages, 7 figures, to be published in "Biomolecular simulations: methods and protocols" edited by Luca Monticelli and Emppu Salonen (Humana Press, 2011)
Mitsutake, Ayori +2 more
openaire +3 more sources
Under-Sampling and Feature Selection Algorithms for S2SMLP
IEEE Access, 2020 Imbalance learning is a hot topic in the data mining and machine learning domains. Data-level, algorithm-level and ensemble solutions are the three main methods proposed thus far to address imbalance learning.
Shudong Liu, Ke Zhang
doaj +1 more source