Search results for "Machine"

showing 10 items of 2592 documents

On the Ambiguous Consequences of Omitting Variables

2015

This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.

Statistics::Machine LearningStatistics::TheoryC51C52BiasMisspecificationLeast-squares estimatorsddc:330Statistics::MethodologyC13Mean squared errorOmitted variablesStatistics::Computation
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On the ambiguous consequences of omitting variables

2015

This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.

Statistics::TheoryMean squared errorjel:C52Regression dilutionjel:C51Local regressionjel:C13Regression analysisOmitted-variable biasCross-sectional regressionStatistics::ComputationOmitted variables Misspecification Least-squares estimators Bias Mean squared errorStatistics::Machine LearningStatisticsEconometricsStatistics::MethodologyRegression diagnosticNonlinear regressionMathematics
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On the sign recovery by LASSO, thresholded LASSO and thresholded Basis Pursuit Denoising

2020

Basis Pursuit (BP), Basis Pursuit DeNoising (BPDN), and LASSO are popular methods for identifyingimportant predictors in the high-dimensional linear regression model Y = Xβ + ε. By definition, whenε = 0, BP uniquely recovers β when Xβ = Xb and β different than b implies L1 norm of β is smaller than the L1 norm of b (identifiability condition). Furthermore, LASSO can recover the sign of β only under a much stronger irrepresentability condition. Meanwhile, it is known that the model selection properties of LASSO can be improved by hard-thresholdingits estimates. This article supports these findings by proving that thresholded LASSO, thresholded BPDNand thresholded BP recover the sign of β in …

Statistics::TheoryStatistics::Machine Learning[STAT.AP]Statistics [stat]/Applications [stat.AP][STAT.AP] Statistics [stat]/Applications [stat.AP]Basis PursuitIdentifiability conditionMultiple regressionStatistics::MethodologyLASSOActive set estimationSign estimationSparsityIrrepresentability condition
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3-D shape reconstruction in an active stereo vision system using genetic algorithms

2003

Abstract The recovery of 3-D shape information (depth) using stereo vision analysis is one of the major areas in computer vision and has given rise to a great deal of literature in the recent past. The widely known stereo vision methods are the passive stereo vision approaches that use two cameras. Obtaining 3-D information involves the identification of the corresponding 2-D points between left and right images. Most existing methods tackle this matching task from singular points, i.e. finding points in both image planes with more or less the same neighborhood characteristics. One key problem we have to solve is that we are on the first instance unable to know a priori whether a point in t…

Stereo camerasbusiness.industryComputer scienceMachine visionEpipolar geometry3D reconstructionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONlaw.inventionStereopsisProjectorArtificial IntelligencelawSignal ProcessingComputer visionComputer Vision and Pattern RecognitionArtificial intelligenceFundamental matrix (computer vision)businessSoftwareComputer stereo visionStereo cameraPattern Recognition
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Structure from motion using a hybrid stereo-vision system

2015

International audience; This paper is dedicated to robotic navigation using an original hybrid-vision setup combining the advantages offered by two different types of camera. This couple of cameras is composed of one perspective camera associated with one fisheye camera. This kind of configuration , is also known under the name of foveated vision system since it is inspired by the human vision system and allows both a wide field of view and a detail front view of the scene. Here, we propose a generic and robust approach for SFM, which is compatible with a very broad spectrum of multi-camera vision systems, suitable for perspective and om-nidirectional cameras, with or without overlapping fi…

Stereo camerasbusiness.industryComputer scienceMachine vision[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO][INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONField of viewStereopsisComputer graphics (images)Structure from motion[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Computer visionSmart cameraArtificial intelligencebusinessComputer stereo visionStereo camera
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Polyamines containing naphthyl groups as pH-regulated molecular machines driven by light

2001

A series of compounds made up by linking methylnaphthalene fragments at both ends of different polyamine chains have shown to behave as pH-regulated molecular machines driven by light and fluorescence emission studies have proved the formation of an excimer between the two naphthalene units whose appearance, fluorescence intensity and decay times depend on the pH value of the media. Albelda Gimeno, Maria Teresa, Teresa.Albelda@uv.es ; Garcia-España Monsonis, Enrique, Enrique.Garcia-Es@uv.es ; Soriano Soto, Concepción, Concepcion.Soriano@uv.es

StereochemistryPHUNESCO::QUÍMICAPhotochemistryExcimerNaphthyl:QUÍMICA [UNESCO]CatalysisFluorescencechemistry.chemical_compoundMaterials ChemistryPolyaminesUNESCO::QUÍMICA::Química orgánicaMethylnaphthaleneNaphthalene:QUÍMICA::Química orgánica [UNESCO]Metals and AlloysGeneral ChemistryFluorescenceMolecular machineSurfaces Coatings and FilmsElectronic Optical and Magnetic MaterialsFluorescence intensitychemistryPolyamines ; Naphthyl ; Methylnaphthalene ; PH ; FluorescenceCeramics and CompositesPolyamine
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Panel Summary One Model for Vision Systems?

1994

This panel reports some considerations about the definition of vision-models. The panellists are scientists working on vision problems from different perspectives. The concept of model in vision seems to remain still open. In fact, it is dynamic, and context dependent. There exists the need for a better exchange of information, among biologists, engineers, physicists, and psychologists in order to improve our knowledge.

StereopsisExchange of informationMachine visionComputer scienceOrder (business)Human–computer interactionExistential quantificationHuman visual system modelContext (language use)
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Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

2022

For the purpose of the Airport Pavement Management System (APMS), in order to optimize the maintenance strategies, it is fundamental monitoring the pavement conditions’ deterioration with time. In this way, the most damaged areas can be detected and intervention can be prioritized. The conventional approach consists in performing non-destructive tests by means of a Heavy Weight Deflectometer (HWD). This equipment allows the measurement of the pavement deflections induced by a defined impact load. This is a quite expensive and time-consuming procedure, therefore, the points to be investigated are usually limited to the center points of a very large mesh grid. Starting from the measured defle…

Stiffness moduluData augmentationAirport pavement; Data augmentation; Machine learning; Non-destructive testing data; Stiffness modulusMachine learningNon-destructive testing dataSettore ICAR/04 - Strade Ferrovie Ed AeroportiAirport pavementAirport pavement; Stiffness modulus; Data augmentation; Machine learning; Non-destructive testing dataStiffness modulus
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A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes

2017

A stimulus class can be composed of perceptually different but functionally equivalent stimuli. The relations between the stimuli that are grouped in a class can be learned or derived from other stimulus relations. If stimulus A is equivalent to B, and B is equivalent to C, then the equivalence between A and C can be derived without explicit training. In this work we propose, with a neurocomputational model, a basic learning mechanism for the formation of equivalence. We also describe how the relatedness between the members of an equivalence class is developed for both trained and derived stimulus relations. Three classic studies on stimulus equivalence are simulated covering typical and at…

Stimulus equivalencePure mathematicslcsh:BF1-990Stimulus (physiology)Machine learningcomputer.software_genre03 medical and health sciencesBasic learning0302 clinical medicinePsychology0501 psychology and cognitive sciences050102 behavioral science & comparative psychologyNodal distanceEquivalence classGeneral PsychologyOriginal ResearchTransitive relationQuantitative Biology::Neurons and Cognitionbusiness.industryneurocomputational modelequivalence classes05 social sciencestransitive relationscategorizationlcsh:PsychologyHebbian theoryCategorizationArtificial intelligenceHebbian learningbusinessPsychologycomputer030217 neurology & neurosurgeryFrontiers in Psychology
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Adaptive sparse representation of continuous input for tsetlin machines based on stochastic searching on the line

2021

This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially h…

Stochastic Searching on the Line automatonBoosting (machine learning)decision support systemTK7800-8360Computer Networks and CommunicationsComputer scienceDiscriminative modelFeature (machine learning)Electrical and Electronic EngineeringArtificial neural networkrule-based learninginterpretable machine learninginterpretable AISparse approximationAutomatonRandom forestSupport vector machineVDP::Teknologi: 500Tsetlin MachineXAIHardware and ArchitectureControl and Systems EngineeringSignal ProcessingElectronicsTsetlin automataAlgorithm
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