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Oriol Ramos Terrades, Salvatore Tabbone, L. Wendling, & Ernest Valveny. (2004). Symbol Recognition based on a Multiresolution Analysis of the Radon Transform.
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Oriol Ramos Terrades, Salvatore Tabbone, & Ernest Valveny. (2006). Combination of shape descriptors using an adaptation of boosting.
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Oriol Ramos Terrades, & Ernest Valveny. (2003). Line Detection Using Ridgelets Transform for Graphic Symbol Representation.
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Oriol Ramos Terrades, & Ernest Valveny. (2003). Indexing Technical Symbols Using Ridgelets Transform.
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Oriol Ramos Terrades, & Ernest Valveny. (2003). Radon Transform for Lineal Symbol Representation.
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Oriol Ramos Terrades, & Ernest Valveny. (2004). Indexing Technical Symbols Using Ridgelets Transform.
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Oriol Ramos Terrades, & Ernest Valveny. (2005). Local Norm Features based on ridgelets Transform.
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Oriol Ramos Terrades, Albert Berenguel, & Debora Gil. (2020). A flexible outlier detector based on a topology given by graph communities.
Abstract: Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.
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Oriol Pujol, Petia Radeva, Jordi Vitria, & J. Mauri. (2004). Adaboost to Classify Plaque Appearance in IVUS Images.
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Oriol Pujol, Petia Radeva, & Jordi Vitria. (2005). Traffic sign recognition using an adaptive boosting multiclass framework.
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Oriol Pujol, Petia Radeva, J. Mauri, & E Fernandez-Nofrerias. (2002). Automatic segmentation of lumen in Intravascular Ultrasound Images: An evaluation of texture feature extractors..
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Oriol Pujol, & Petia Radeva. (2002). Lumen Detection in Ivus Image Using Snakes in a Statical Framework..
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Oriol Pujol, & Petia Radeva. (2006). Optimal extension of Error Correcting Output Codes.
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Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, E. Fernandez, V. Valle, & Petia Radeva. (2004). Automatic segmentation and characterization of IVUS images by texture analysis.
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Oriol Pujol, Misael Rosales, Petia Radeva, & E Fernandez-Nofrerias. (2003). Intravascular Ultrasound Images Vessel Characterization using AdaBoost.
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