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Ricerca accademica
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem
Testo / risorsa
Testo / risorsa
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem
Vilanova, Gabriela et al · SEDICI UNLP · 2001
Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multipl...
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Performance evaluation of the parallel polytree approximation distribution algorithm on three network technologies
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Testo / risorsa
Performance evaluation of the parallel polytree approximation distribution algorithm on three network technologies
Madera, Julio et al · SEDICI UNLP · 2006
This paper proposes two parallel variants of an Estimation of Distribution Algorithm (EDA) that represents the probability distribution by means of a single connected graphical model based on a polytree structure. The ma...
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Solving unrestricted parallel machine scheduling problems via evolutionary algorithms
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Solving unrestricted parallel machine scheduling problems via evolutionary algorithms
Gatica, Claudia Ruth et al · SEDICI UNLP · 2003
Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model invol...
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$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Articolo
Articolo
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Michal Aharon; Michael Elad; Alfred M. Bruckstein⋆ · IEEE Transactions on Signal Processing · 2006
In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinatio...
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A Fast Learning Algorithm for Deep Belief Nets
Articolo
Articolo
A Fast Learning Algorithm for Deep Belief Nets
Geoffrey E. Hinton; Simon Osindero; Yee‐Whye Teh · Neural Computation · 2006
We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast...
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A Greedy Algorithm for Aligning DNA Sequences
Articolo
Articolo
A Greedy Algorithm for Aligning DNA Sequences
Zheng Zhang; Scott Schwartz; Lukas Wagner; Webb Miller · Journal of Computational Biology · 2000
For aligning DNA sequences that differ only by sequencing errors, or by equivalent errors from other sources, a greedy algorithm can be much faster than traditional dynamic programming approaches and yet produce an align...
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A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
Articolo
Articolo
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
Stéphane Guindon; Olivier Gascuel · Systematic Biology · 2003
The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on th...
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A Singular Value Thresholding Algorithm for Matrix Completion
Articolo
Articolo
A Singular Value Thresholding Algorithm for Matrix Completion
Jian‐Feng Cai; Emmanuel J. Candès; Zuowei Shen · SIAM Journal on Optimization · 2010
Abstract. This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a r...
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A fast and elitist multiobjective genetic algorithm: NSGA-II
Articolo
Articolo
A fast and elitist multiobjective genetic algorithm: NSGA-II
Kalyanmoy Deb; Amrit Pratap; Sakshi Agarwal; T. Meyarivan · IEEE Transactions on Evolutionary Computation · 2002
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is th...
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A grid-based Bader analysis algorithm without lattice bias
Articolo
Articolo
A grid-based Bader analysis algorithm without lattice bias
Wenjie Tang; Edward Sanville; Graeme Henkelman · Journal of Physics Condensed Matter · 2009
A computational method for partitioning a charge density grid into Bader volumes is presented which is efficient, robust, and scales linearly with the number of grid points. The partitioning algorithm follows the steepes...
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An Algorithm for Least-Squares Estimation of Nonlinear Parameters
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Articolo
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Donald W. Marquardt · Journal of the Society for Industrial and Applied Mathematics · 1963
Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBibTexSections ToolsAdd to favoritesExport Ci...
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An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Articolo
Articolo
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb; Himanshu Jain · IEEE Transactions on Evolutionary Computation · 2013
Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing...
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An efficient k-means clustering algorithm: analysis and implementation
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Articolo
An efficient k-means clustering algorithm: analysis and implementation
Tapas Kanungo; David M. Mount; Nathan S. Netanyahu; Christine Piatko; Ruth Silverman; Angela Y. Wu · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2002
In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared dist...
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Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
Articolo
Articolo
Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
Garrett M. Morris; David S. Goodsell; Robert S. Halliday; Ruth Huey; William E. Hart; Richard K. Belew; Arthur J. Olson · Journal of Computational Chemistry · 1998
A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the...
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Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
Articolo
Articolo
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
Hude Quan; Vijaya Sundararajan; Patricia Halfon; Andrew Fong; Bernard Burnand; Jean‐Christophe Luthi; L. Duncan Saunders; Cynthia A Beck · Medical Care · 2005
OBJECTIVES: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this,...
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Articolo
Articolo
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan; Lily Peng; Marc Coram; Martin C. Stumpe; Derek Wu; Arunachalam Narayanaswamy; Subhashini Venugopalan; Kasumi Widner · JAMA · 2016
Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules ...
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Dynamic programming algorithm optimization for spoken word recognition
Articolo
Articolo
Dynamic programming algorithm optimization for spoken word recognition
Hiroaki Sakoe; Seibi Chiba · IEEE Transactions on Acoustics Speech and Signal Processing · 1978
This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two t...
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Factor graphs and the sum-product algorithm
Articolo
Articolo
Factor graphs and the sum-product algorithm
Frank R. Kschischang; Brendan J. Frey; Hans‐Andrea Loeliger · IEEE Transactions on Information Theory · 2001
Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of "local" functions, each of which depends on a subset of the varia...
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GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
Articolo
Articolo
GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
Youcef Saad; Martin H. Schultz · SIAM Journal on Scientific and Statistical Computing · 1986
We present an iterative method for solving linear systems, which has the property ofminimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for c...
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MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Articolo
Articolo
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang; Hui Li · IEEE Transactions on Evolutionary Computation · 2007
Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algori...
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Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Articolo
Articolo
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
A. P. Dempster; N. M. Laird; Donald B. Rubin · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1977
Summary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence...
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Phase retrieval algorithms: a comparison
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Articolo
Phase retrieval algorithms: a comparison
James R. Fienup · Applied Optics · 1982
Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods. Both the problem of phase retrieval from two intensity measurements (in electron microscopy or wave front sensing) and...
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Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
Articolo
Articolo
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
Y. Zhang; Michael Brady; Stephen M. Smith · IEEE Transactions on Medical Imaging · 2001
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain ...
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The viterbi algorithm
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Articolo
The viterbi algorithm
G. David Forney · Proceedings of the IEEE · 1973
The Viterbi algorithm (VA) is a recursive optimal solution to the problem of estimating the state sequence of a discrete-time finite-state Markov process observed in memoryless noise. Many problems in areas such as digit...
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Pubblicazione seriale

3D cross-hole resistivity inversion imaging of surrounding rock based on distance weighting constraint algorithm

ISSN: 10004548 · Editore / fonte: Editorial Office of Chinese Journal of Geotechnical Engineering

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