Search results for "K-nearest neighbors"

showing 10 items of 54 documents

Semisupervised nonlinear feature extraction for image classification

2012

Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…

Graph kernelComputer scienceFeature extractioncomputer.software_genreKernel principal component analysisk-nearest neighbors algorithmKernel (linear algebra)Polynomial kernelPartial least squares regressionLeast squares support vector machineCluster analysisTraining setContextual image classificationbusiness.industryDimensionality reductionPattern recognitionManifoldKernel methodKernel embedding of distributionsKernel (statistics)Principal component analysisRadial basis function kernelPrincipal component regressionData miningArtificial intelligencebusinesscomputer2012 IEEE International Geoscience and Remote Sensing Symposium
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Discussion of “Soil Water Retention Characteristics of Vertisols and Pedotransfer Functions Based on Nearest Neighbor and Neural Networks Approaches …

2013

HYDRAULIC PROPERTIESArtificial neural networkPREDICTIONSWRCSoil scienceSoil Water Retention Curve Soil Shrinkage Characteristic CurveVertisolHYDRAULIC PROPERTIES; SHRINKAGE; PREDICTION; SWRC; ANNAgricultural and Biological Sciences (miscellaneous)k-nearest neighbors algorithmPedotransfer functionSoil waterSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSHRINKAGEANNWater Science and TechnologyCivil and Structural EngineeringMathematics
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Some Experiments in Supervised Pattern Recognition with Incomplete Training Samples

2002

This paper presents some ideas about automatic procedures to implement a system with the capability of detecting patterns arising from classes not represented in the training sample. The procedure aims at incorporating automatically to the training sample the necessary information about the new class for correctly recognizing patterns from this class in future classification tasks. The Nearest Neighbor rule is employed as the central classifier and several techniques are added to cope with the peril of incorporating noisy data to the training sample. Experimental results with real data confirm the benefits of the proposed procedure.

Information extractionComputer sciencebusiness.industryAnomaly detectionPattern recognitionArtificial intelligencebusinessMachine learningcomputer.software_genreClassifier (UML)computerk-nearest neighbors algorithm
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Spintronic properties of Li1.5Mn0.5Z (Z=As, Sb) compounds in the Cu2Sb structure

2015

Abstract We have investigated the spintronic properties of two formula units of Li1.5Mn0.5Z (Z=As, Sb), in the Cu2Sb tetragonal crystal structure based on first-principles density-functional theory calculations, at, and near, their equilibrium (minimum total energy) lattice constants. Two groups of configurations, A and B, are formed for each type of alloy by interchanging Mn with each Li located at four different positions with respect to Li4Z2. Mn has four nearest neighbors in group-A and has one nearest neighbor in group-B. The bonding features of the alloys are compared to the ionic bonding in Li4Z2, and the tetragonal structure of cubic LiMnZ. The magnetic moments of these compounds ar…

Materials scienceSpintronicsMagnetic momentCondensed matter physicsIonic bondingCondensed Matter Physicsk-nearest neighbors algorithmElectronic Optical and Magnetic MaterialsBohr magnetonCrystallographyTetragonal crystal systemsymbols.namesakeLattice constantFerromagnetismsymbolsJournal of Magnetism and Magnetic Materials
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Evidence against a glass transition in the 10-state short range Potts glass

2002

We present the results of Monte Carlo simulations of two different 10-state Potts glasses with random nearest neighbor interactions on a simple cubic lattice. In the first model the interactions come from a \pm J distribution and in the second model from a Gaussian one, and in both cases the first two moments of the distribution are chosen to be equal to J_0=-1 and Delta J=1. At low temperatures the spin autocorrelation function for the \pm J model relaxes in several steps whereas the one for the Gaussian model shows only one. In both systems the relaxation time increases like an Arrhenius law. Unlike the infinite range model, there are only very weak finite size effects and there is no evi…

PhysicsArrhenius equationStatistical Mechanics (cond-mat.stat-mech)GaussianMonte Carlo methodAutocorrelationFOS: Physical sciencesGeneral Physics and AstronomyDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural Networksk-nearest neighbors algorithmsymbols.namesakesymbolsStatistical physicsGlass transitionGaussian network modelCondensed Matter - Statistical MechanicsSpin-½
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Positive Tolman Length in a Lattice Gas with Three-Body Interactions

2011

We present a new method to determine the curvature dependence of the interface tension between coexisting phases in a finite volume from free energies obtained by Monte Carlo simulations. For the example of a lattice gas on a 3D fcc lattice with nearest neighbor three-body interactions, we demonstrate how to calculate the equimolar radius ${R}_{e}$ as well as the radius ${R}_{s}$ of the surface of tension and thus the Tolman length $\ensuremath{\delta}({R}_{s})={R}_{e}\ensuremath{-}{R}_{s}$. Within the physically relevant range of radii, $\ensuremath{\delta}({R}_{s})$ shows a pronounced ${R}_{s}$ dependence, such that the simple Tolman parametrization for the interface tension is refutable.…

PhysicsCondensed matter physicsLattice (order)ExtrapolationGeneral Physics and AstronomyTolman lengthFree energiesLimitingRadiusCurvaturek-nearest neighbors algorithmPhysical Review Letters
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Nearest-neighbor Ising antiferromagnet on the fcc lattice: Evidence for multicritical behavior.

1996

The phase behavior of the Ising model with nearest-neighbor antiferromagnetic interactions on the fcc lattice in a homogeneous magnetic field is studied by means of large-scale Monte Carlo simulations. In accordance with the most recent of the previous investigations, but with significantly higher accuracy, it is found that the ``triple'' point at which the disordered phase coexists with both the AB phase as well as with the ${\mathit{A}}_{3}$B phase (corresponding to the model's lattice gas interpretation as a binary alloy ${\mathit{A}}_{\mathit{xB}1\mathrm{\ensuremath{-}}\mathit{x}}$ such as ${\mathrm{Cu}}_{\mathit{x}}$${\mathrm{Au}}_{1\mathrm{\ensuremath{-}}\mathit{x}}$) occurs at a nonz…

PhysicsCondensed matter physicsTriple pointLattice (order)AntiferromagnetismIsing modelMulticritical pointLattice model (physics)Landau theoryk-nearest neighbors algorithmPhysical review. B, Condensed matter
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Numerical simulation of free dissipative open quantum system and establishment of a formula for π

2020

We transform the system/reservoir coupling model into a one-dimensional semi-infinite discrete chain with nearest neighbor interaction through a unitary transformation, and, simulate the dynamics of free dissipative open quantum system. We investigate the consequences of such modeling, which is observed as finite size effect causing the recurrence of particle from the end of the chain. Afterwards, we determine a formula for π in terms of the matrix operational form, which indicates a robustness of the connection between quantum physics and basic mathematics. peerReviewed

PhysicsCouplingComputer simulationUnitary transformationk-nearest neighbors algorithmtiiviin aineen fysiikkaOpen quantum systemMatrix (mathematics)Classical mechanicscondensed matter physicsChain (algebraic topology)Dissipative systemsimulointikvanttifysiikka
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Spin and charge orderings in the atomic limit of the U-V-J model

2011

In this paper we study a generalization of the 1D Hubbard model by considering density-density and Ising-type spin-spin nearest neighbor (NN) interactions, parameterized by $V$ and $J$, respectively. We present the T=0 phase diagram for both ferro ($J>0$) and anti-ferro ($J<0$) coupling obtained in the narrow-band limit by means of an extension to zero-temperature of the transfer-matrix method. Based on the values of the Hamiltonian parameters, we identify a number of phases that involve orderings of the double occupancy, NN density and spin correlations, being these latter very fragile.

PhysicsHistoryHubbard modelStrongly Correlated Electrons (cond-mat.str-el)Condensed Matter - SuperconductivityParameterized complexityFOS: Physical sciencesComputer Science ApplicationsEducationk-nearest neighbors algorithmSuperconductivity (cond-mat.supr-con)symbols.namesakeCondensed Matter - Strongly Correlated ElectronssymbolsCondensed Matter::Strongly Correlated ElectronsHamiltonian (quantum mechanics)Mathematical physicsPhase diagram
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Green functions for nearest- and next-nearest-neighbor hopping on the Bethe lattice

2005

We calculate the local Green function for a quantum-mechanical particle with hopping between nearest and next-nearest neighbors on the Bethe lattice, where the on-site energies may alternate on sublattices. For infinite connectivity the renormalized perturbation expansion is carried out by counting all non-self-intersecting paths, leading to an implicit equation for the local Green function. By integrating out branches of the Bethe lattice the same equation is obtained from a path integral approach for the partition function. This also provides the local Green function for finite connectivity. Finally, a recently developed topological approach is extended to derive an operator identity whic…

PhysicsImplicit functionBethe latticeStrongly Correlated Electrons (cond-mat.str-el)Operator (physics)Spectrum (functional analysis)General Physics and AstronomyFOS: Physical sciencesPartition function (mathematics)01 natural sciences010305 fluids & plasmask-nearest neighbors algorithmCondensed Matter - Strongly Correlated Electrons0103 physical sciencesPath integral formulationGravitational singularityddc:530Condensed Matter::Strongly Correlated ElectronsStatistical physics010306 general physics
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