0000000000328806

AUTHOR

Gabriel Gómez-pérez

showing 3 related works from this author

Perceptual Image Representations for Support Vector Machine Image Coding

2007

Support-vector-machine image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regressor (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image-coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of t…

Computer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionImage processingPermissionImage (mathematics)Support vector machineAutomatic image annotationDigital image processingComputer visionArtificial intelligenceImage warpingbusinessFeature detection (computer vision)
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Perceptual adaptive insensitivity for support vector machine image coding.

2005

Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant epsilon-insensitivity zone by Robinson and Kecman. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes sense in the spatial domain but it is certainly not a good option in a frequency domain. In fact, in their approach, they made a fixed low-pass assumption as the number of discrete cosine transform (DCT) coefficients to be used in the training was limited. This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs [2] for image coding u…

Computer Networks and CommunicationsImage processingPattern Recognition AutomatedArtificial IntelligenceDistortionImage Interpretation Computer-AssistedDiscrete cosine transformComputer SimulationMathematicsModels StatisticalArtificial neural networkbusiness.industryPattern recognitionSignal Processing Computer-AssistedGeneral MedicineData CompressionComputer Science ApplicationsSupport vector machineFrequency domainVisual PerceptionA priori and a posterioriArtificial intelligencebusinessSoftwareAlgorithmsImage compressionIEEE transactions on neural networks
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Assigning discounts in a marketing campaign by using reinforcement learning and neural networks

2009

In this work, RL is used to find an optimal policy for a marketing campaign. Data show a complex characterization of state and action spaces. Two approaches are proposed to circumvent this problem. The first approach is based on the self-organizing map (SOM), which is used to aggregate states. The second approach uses a multilayer perceptron (MLP) to carry out a regression of the action-value function. The results indicate that both approaches can improve a targeted marketing campaign. Moreover, the SOM approach allows an intuitive interpretation of the results, and the MLP approach yields robust results with generalization capabilities.

Artificial neural networkComputer scienceGeneralizationbusiness.industrymedia_common.quotation_subjectAggregate (data warehouse)General EngineeringMachine learningcomputer.software_genreComputer Science ApplicationsFunction approximationArtificial IntelligenceMultilayer perceptronReinforcement learningState (computer science)Artificial intelligenceFunction (engineering)businesscomputermedia_commonExpert Systems with Applications
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