Golge, Eren2015-10-132015-10-132014-082014-08http://hdl.handle.net/11693/14002Includes bibliographical references (leaves 56-64).Thesis (M.S.): Bilkent University, The Department of Computer Engineering and the Graduate School of Engineering and Science, 2014.Cataloged from PDF version of thesis.We attack the problem of learning concepts automatically from noisy Web image search results. The idea is based on discovering common characteristics shared among category images by posing two novel methods that are able to organise the data while eliminating irrelevant instances. We propose a novel clustering and outlier detection method, namely Concept Map (CMAP). Given an image collection returned for a concept query, CMAP provides clusters pruned from outliers. Each cluster is used to train a model representing a different characteristics of the concept. One another method is Association through Model Evolution (AME). It prunes the data in an iterative manner and it progressively finds better set of images with an evaluational score computed for each iteration. The idea is based on capturing discriminativeness and representativeness of each instance against large number of random images and eliminating the outliers. The final model is used for classification of novel images. These two methods are applied on different benchmark problems and we observed compelling or better results compared to state of art methods.xiii, 64 leaves : illustrations, graphics.Englishinfo:eu-repo/semantics/openAccessWeakly-supervised learningConcept learningRectifying self-organizing mapAssociation with model evolutionClustering and outlier detectionConceptmapAttributesObject recognitionScene classificationFace identificationFeature learningQA76 .G653 2014Computer science.Data mining.ThesisB148256