Search results for "Composite kernels"

showing 2 items of 2 documents

Unsupervised change detection with kernels

2012

In this paper an unsupervised approach to change detection relying on kernels is introduced. Kernel based clustering is used to partition a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained the estimated representatives (centroids) of each group are used to assign the class membership to all others pixels composing the multitemporal scenes. Different approaches of considering the multitemporal information are considered with accent on the computation of the difference image directly in the feature spaces. For this purpose a difference kernel approach is successfully adopted. Finally an effective way to cope with the estimation o…

Correctness010504 meteorology & atmospheric sciencesFeature extraction0211 other engineering and technologiesComposite kernels02 engineering and technologykernel parameters01 natural sciencesunsupervised change detectionElectrical and Electronic Engineeringkernel k-meansCluster analysis021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsPixelbusiness.industryPattern recognitionGeotechnical Engineering and Engineering GeologyNonlinear systemKernel (image processing)Unsupervised learningArtificial intelligencebusinessChange detectionIEEE Geoscience and Remote Sensing Letters
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Non-linear System Identification with Composite Relevance Vector Machines

2007

Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection. Teoría de la Señal y Comunicaciones

Relevance Vector MachinesTelecomunicacionesNonlinear system identificationbusiness.industryRVMApplied MathematicsNonlinear System IdentificationRegression analysisPattern recognitionComposite kernelsFunction (mathematics)Support vector machineNonlinear systemStatistics::Machine LearningSignal ProcessingBenchmark (computing)3325 Tecnología de las TelecomunicacionesRelevance (information retrieval)Artificial intelligenceElectrical and Electronic EngineeringbusinessMathematicsFree parameter
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