Search results for " Support vector machines"

showing 6 items of 6 documents

Structured Output SVM for Remote Sensing Image Classification

2011

Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…

Computer scienceMultispectral imageTheoretical Computer ScienceSet (abstract data type)Kernel (linear algebra)One-class classificationRemote sensingSupport vector machinesStructured support vector machinePixelContextual image classificationbusiness.industryKernel methodsPattern recognitionLand use classificationSupport vector machineTree (data structure)Kernel methodHardware and ArchitectureControl and Systems EngineeringModeling and SimulationKernel (statistics)Radial basis function kernelSignal ProcessingStructured output learningArtificial intelligenceTree kernelStructured output learning; Support vector machines; Kernel methods; Land use classificationbusinessInformation SystemsJournal of Signal Processing Systems
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Least-Norm Regularization For Weak Two-Level Optimization Problems

1992

In this paper, we consider a regularization for weak two-level optimization problems by adaptation of the method presented by Solohovic (1970). Existence and approximation results are given in the case in which the constraints to the lower level problems are described by a multifunction. Convergence results for the least-norm regularization under perturbations are also presented.

Mathematical optimizationOptimization problemNorm (mathematics)Proximal gradient methods for learningRegularization perspectives on support vector machinesBackus–Gilbert methodRegularization (mathematics)Mathematics
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Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification

2019

Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-…

Crohn's diseasemedicine.diagnostic_testComputer sciencebusiness.industryFeature vectorFeature extractionSupervised learningMagnetic resonance imagingPattern recognitionmedicine.diseaseCrohn’s disease classification Feature extraction Feature reduction K-fold cross-validation Supervised learning Support vector machinesSupport vector machinemedicineMedical imagingArtificial intelligencebusinessReliability (statistics)
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Regularization operators for natural images based on nonlinear perception models.

2006

Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator take…

Regularization perspectives on support vector machinesInformation Storage and RetrievalImage processingRegularization (mathematics)Pattern Recognition AutomatedOperator (computer programming)Artificial IntelligenceImage Interpretation Computer-AssistedCluster AnalysisComputer SimulationImage restorationMathematicsModels Statisticalbusiness.industryWavelet transformSpectral density estimationStatistical modelPattern recognitionNumerical Analysis Computer-AssistedSignal Processing Computer-AssistedImage EnhancementComputer Graphics and Computer-Aided DesignNonlinear DynamicsArtificial intelligencebusinessSoftwareAlgorithmsIEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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On the discrete linear ill‐posed problems

1999

An inverse problem of photo‐acoustic spectroscopy of semiconductors is investigated. The main problem is formulated as the integral equation of the first kind. Two different regularization methods are applied, the algorithms for defining regularization parameters are given. Diskrečiųjų blogai sąlygotų uždavinių klausimu Santrauka Darbe nagrinejamas foto‐akustines spektroskopijos puslaidininkiuose uždavinys, kuriame i vertinami nešeju difuzijos ir rekombinacijos procesai. Reikia atstatyti šaltinio funkcija f(x), jei žinoma antrosios eiles difuzijos lygtis ir atitinkamos kraštines salygos. Naudojantis matavimu, atliktu ivairiuose dažniuose, rezultatais sprendžiamas atvirkštinis uždavinys, kel…

Well-posed problemMathematical analysisRegularization perspectives on support vector machinesBackus–Gilbert method-Inverse problemIntegral equationRegularization (mathematics)Tikhonov regularizationModeling and SimulationInverse scattering problemQA1-939Applied mathematicsMathematicsAnalysisMathematicsMathematical Modelling and Analysis
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3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

2022

Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radio-mics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a …

Breast cancer Dynamic contrast-enhanced magnetic resonance imagingSupport Vector MachineComputer scienceNormalization (image processing)Breast NeoplasmsFeature selectionBreast cancerBreast cancerDiscriminative modelmedicineHumansRadiology Nuclear Medicine and imagingBreastRetrospective StudiesDynamic contrast-enhanced magnetic resonance imagingRadiomicsSupport vector machinesReceiver operating characteristicbusiness.industryPattern recognitionmedicine.diseaseMagnetic Resonance Imagingmachine learning Radiomics unsupervised feature selection Support vector machinesSupport vector machinemachine learningROC CurveFeature (computer vision)Test setFemaleArtificial intelligenceSettore MED/36 - Diagnostica Per Immagini E Radioterapiabusinessunsupervised feature selectionBreast cancer Dynamic contrast-enhanced magnetic resonance imaging; machine learning Radiomics unsupervised feature selection Support vector machinesAcademic Radiology
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