Search results for "least squares"

showing 10 items of 268 documents

Fuzzy sigmoid kernel for support vector classifiers

2004

This Letter proposes the use of the fuzzy sigmoid function presented in (IEEE Trans. Neural Networks 14(6) (2003) 1576) as non-positive semi-definite kernel in the support vector machines framework. The fuzzy sigmoid kernel allows lower computational cost, and higher rate of positive eigenvalues of the kernel matrix, which alleviates current limitations of the sigmoid kernel.

business.industryCognitive NeurosciencePattern recognitionSigmoid functionFuzzy logicComputer Science ApplicationsSupport vector machineKernel methodArtificial IntelligencePolynomial kernelKernel embedding of distributionsRadial basis function kernelLeast squares support vector machineArtificial intelligencebusinessMathematicsNeurocomputing
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Semisupervised kernel orthonormalized partial least squares

2012

This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…

business.industryFeature extractionNonlinear dimensionality reductionPattern recognitionComputingMethodologies_PATTERNRECOGNITIONKernel methodVariable kernel density estimationKernel (statistics)Radial basis function kernelPartial least squares regressionArtificial intelligenceCluster analysisbusinessMathematics2012 IEEE International Workshop on Machine Learning for Signal Processing
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Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis

2019

Real-world data are often linked with each other since they share some common characteristics. The mutual linking can be seen as a core driving force of group analysis. This study proposes a generalized linked canonical polyadic tensor decomposition (GLCPTD) model that is well suited to exploiting the linking nature in multi-block tensor analysis. To address GLCPTD model, an efficient algorithm based on hierarchical alternating least squa res (HALS) method is proposed, termed as GLCPTD-HALS algorithm. The proposed algorithm enables the simultaneous extraction of common components, individual components and core tensors from tensor blocks. Simulation experiments of synthetic EEG data analysi…

canonical polyadicComputer scienceGeneralizationNoise reductionlinked tensor decomposition020206 networking & telecommunications02 engineering and technologyIterative reconstructionhierarchical alternating least squares03 medical and health sciencessimultaneous extraction0302 clinical medicineGroup analysisCore (graph theory)0202 electrical engineering electronic engineering information engineeringTensor decompositionTensorAlgorithmRealization (systems)030217 neurology & neurosurgery
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Use of pH gradients in continuous-flow systems and multivariate regression techniques applied to the determination of methionine and cysteine in phar…

1997

Abstract The simultaneous spectrophotometric determination of methionine and cysteine in presence of cystine and other compounds in pharmaceuticals, using a multivariate calibration method, was studied. The method is based on the reaction between the analytes and the o- phthalaldehyde -N- acetyl - l - cysteine (OPA-NAC) reagent performed in a continuous-flow system (FI). The FI system allows the generation of a local pH gradient in order to produce spectral and/or kinetic changes in the UV-Vis spectra of the amino acid-OPA-NAC derivatives. This information is used to improve the prediction ability of the Partial Least-Squares (PLS) models. The performance of two FI assemblies, the selection…

chemistry.chemical_classificationAnalyteMethionineChromatographyCystineBiochemistryAnalytical ChemistryAmino acidchemistry.chemical_compoundchemistryReagentPartial least squares regressionEnvironmental ChemistrySpectroscopyCysteinePhthalaldehydeAnalytica Chimica Acta
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Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy

2017

International audience; Near-infrared (NIR) spectroscopy was evaluated as a rapid method for the determination of oleic, palmitic, linoleic and linolenic acids as well as omega-3, omega-6, and to predict polyunsaturated, monounsaturated and saturated fatty acids, together with triacylglycerides, diglycerides, free fatty acids and ergosterol in salmon oil. To do it, Partial Least Squares (PLS) regression models were applied to correlate NIR spectra with aforementioned fatty acids and lipid classes. Results obtained were validated in front of reference procedures based on high performance thin layer and gas chromatography. PLS-NIR has a good predictive capability with relative root mean squar…

classe lipidique[SDV]Life Sciences [q-bio]Predictive capabilityLipid classPartial least square01 natural sciencesSalmon oilAnalytical Chemistrychemistry.chemical_compoundFish Oils0404 agricultural biotechnologyPartial least squares regression[SDV.IDA]Life Sciences [q-bio]/Food engineeringOrganic chemistry[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process EngineeringLeast-Squares AnalysisFatty acidsSpectroscopyOmega-6évaluation de méthodeOmega-3ErgosterolSpectroscopy Near-InfraredChromatographyacide gras010401 analytical chemistryNear-infrared spectroscopyoméga 3traitement statistique04 agricultural and veterinary sciencesGeneral Medicine040401 food science0104 chemical sciencessalmo salarNear infrared spectroscopy;Partial least square;Fatty acids;Lipid class;Omega-3;Omega-6chemistryNir spectraGas chromatographyspectroscopie proche infrarougeoméga 6Near infrared spectroscopyFood Sciencehuile de poisson
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Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological N…

2020

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state&ndash

conditional transfer entropyInformation transferlinear predictionDynamical systems theoryComputer scienceState–space modelsGeneral Physics and Astronomylcsh:AstrophysicsNetwork topologycomputer.software_genrenetwork physiology01 natural sciencesArticle03 medical and health sciences0302 clinical medicinepenalized regression techniquelcsh:QB460-4660103 physical sciencesEntropy (information theory)Statistics::Methodologylcsh:Science010306 general physicspartial information decompositionmultivariate time series analysisinformation dynamics; partial information decomposition; entropy; conditional transfer entropy; network physiology; multivariate time series analysis; State–space models; vector autoregressive model; penalized regression techniques; linear predictionState–space modellcsh:QC1-999multivariate time series analysiInformation dynamicData pointpenalized regression techniquesAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelOrdinary least squaresvector autoregressive modellcsh:QData mininginformation dynamicsentropycomputerlcsh:Physics030217 neurology & neurosurgery
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Search strategies in innovation networks: The case of the Hungarian food industry

2020

In the food sector, open innovation has become of particular interest. This paper considers open innovation search strategies in the food and beverages industry and examines the probability of using different innovation sources with respect to the type of innovation. Although the information search for new ideas, tools and solutions in the innovation process regarding the scope and depth is well explored and interpreted in the literature, the probability of using the different sources with respect to type of innovation is rarely investigated. To answer these questions, first a probit, then OLS regression model is adopted, in order to understand the chance of a specific source of information…

food industryFood industryProcess (engineering)policy implicationlcsh:TJ807-830Geography Planning and Developmentlcsh:Renewable energy sourcesProbitManagement Monitoring Policy and Lawinnovation sourcing strategyOrder (exchange)0502 economics and businessSettore AGR/01 - Economia Ed Estimo Ruralelcsh:Environmental sciencesIndustrial organizationOpen innovationlcsh:GE1-350HungaryScope (project management)Renewable Energy Sustainability and the Environmentbusiness.industrylcsh:Environmental effects of industries and plants05 social sciencesinnovation networkProduct (business)lcsh:TD194-195Ordinary least squaresFood industry Hungary Innovation network Innovation sourcing strategy Policy implication050211 marketingBusiness050203 business & management
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Analysis of Caffeine, Sweeteners, and Other Additives in Beverages by Vibrational Spectroscopy

2001

This chapter presents a review of the scientific literature on the use of vibrational spectroscopy, near-infrared (NIR), mid-infrared (mid-IR), and Raman, for the analysis of caffeine, sweeteners, and other additives in beverages and related products. Direct analysis procedures of coffee and tea, for both classification according to precedence or variety and quantitative determination of caffeine, are available. For beverage analysis, caffeine has been determined by direct attenuated total reflection (ATR) measurement or by transmission spectroscopy in the mid-IR region after extraction with chloroform. Different strategies have been employed for the analysis of sweeteners in beverages and …

food.ingredientChromatographyAspartameFood additiveAnalytical chemistryInfrared spectroscopysymbols.namesakechemistry.chemical_compoundfoodchemistryAttenuated total reflectionPartial least squares regressionsymbolsRaman spectroscopyCaffeineSpectroscopyHandbook of Vibrational Spectroscopy
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Impact of the terrestrial reference frame on the determination of the celestial reference frame.

2022

Currently three up-to-date Terrestrial Reference Frames (TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station positions and Earth orientation parameters, but the concept of combining these data is fundamentally different. The IGN approach is based on the combination of technique solutions, while the DGFI is combining the normal equation systems. Both yield in reference epoch coordinates and velocities for a global set of stations. JPL uses a Kalman filter approach, realizing a TRF through weekly time series of geocentric coordinates. As the determination of the CRF is not independent of the T…

lcsh:QB275-343010504 meteorology & atmospheric sciencesEpoch (astronomy)lcsh:Geodesylcsh:QC801-809Kalman filter010502 geochemistry & geophysicsGeodesyMissing data01 natural sciencesGeocentric coordinateslcsh:Geophysics. Cosmic physicsGeophysicsPosition (vector)Computers in Earth SciencesTerrestrial reference frameLinear least squares0105 earth and related environmental sciencesEarth-Surface ProcessesReference frameMathematicsGeodesy and geodynamics
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System identification via optimised wavelet-based neural networks

2003

Nonlinear system identification by means of wavelet-based neural networks (WBNNs) is presented. An iterative method is proposed, based on a way of combining genetic algorithms (GAs) and least-square techniques with the aim of avoiding redundancy in the representation of the function. GAs are used for optimal selection of the structure of the WBNN and the parameters of the transfer function of its neurones. Least-square techniques are used to update the weights of the net. The basic criterion of the method is the addition of a new neurone, at a generic step, to the already constructed WBNN so that no modification to the parameters of its neurones is required. Simulation experiments and compa…

least squares approximations nonlinear dynamical systems identification neural nets iterative methods genetic algorithmsQuantitative Biology::Neurons and CognitionArtificial neural networkNonlinear system identificationIterative methodComputer scienceSystem identificationTransfer functionWaveletSettore ING-INF/04 - AutomaticaControl and Systems EngineeringControl theoryRedundancy (engineering)Electrical and Electronic EngineeringRepresentation (mathematics)InstrumentationAlgorithmIEE Proceedings - Control Theory and Applications
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