Search results for "One-class classification"

showing 4 items of 4 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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Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation

2007

We propose a method of real-time implementation of an approximation of the support vector machine decision rule. The method uses an improvement of a supervised classification method based on hyperrectangles, which is useful for real-time image segmentation. We increase the classification and speed performances using a combination of classification methods: a support vector machine is used during a pre-processing step. We recall the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present our learning step combination algorithm and results obtained using Gaussian distributions and an example of image segmentation coming from a part …

Computer sciencebusiness.industryGaussianScale-space segmentationPattern recognitionImage processingLinear classifierImage segmentationDecision ruleMachine learningcomputer.software_genreSupport vector machinesymbols.namesakesymbolsOne-class classificationArtificial intelligencebusinesscomputerGaussian process
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A Study on Classification Methods Applied to Sentiment Analysis

2013

Sentiment analysis is a new area of research in data mining that concerns the detection of opinions and/or sentiments in texts. This work focuses on the application and the comparison of three classification techniques over a text corpus composed of reviews of commercial products in order to detect opinions about them. The chosen domain is about "perfumes", and user opinions composing the corpus are written in Italian language. The proposed approach is completely data-driven: a Term Frequency / Inverse Document Frequency (TFIDF) terms selection procedure has been applied in order to make computation more efficient, to improve the classification results and to manage some issues related to t…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniText corpusNaive Bayes classifierComputer sciencebusiness.industrySentiment analysisTF-IDFSentiment Classificationcomputer.software_genreClass Association RulesDomain (software engineering)Naive Bayes classifierRandom indexingArtificial IntelligenceSelection (linguistics)One-class classificationArtificial intelligenceRandom Indexingbusinesstf–idfcomputerNatural language processing
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A one class classifier for Signal identification: a biological case study

2008

The paper describes an application of a one-class KNN to identify different signal patterns embedded in a noise structured background. The problem become harder whenever only one pattern is well represented in the signal, in such cases one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM) that provides a preliminary signal segmentation in an interval feature space. The one-class KNN has been tested on synthetic data that simulate microarray data for the identification of nucleosomes and linker regions across DNA. Results have shown a good recognition rate on synthetic data for nucleosome and lin…

business.industryComputer scienceFeature vectorOne-class classificationPattern recognitionSegmentationArtificial intelligencebusinessMulti Layer Method One Class classification Bioinformatics Nucleosome Positioning.Classifier (UML)Synthetic data
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