Área metropolitana de Santiago
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10+ years building software.

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Publicaciones

  • ORAND at TRECVID 2014: Instance Search and Multimedia Event Detection

    TRECVID, NIST

    ORAND S.A. is a Chilean company focused on developing applied research in Computer Science. This report describes the participation of the ORAND team at Instance Search task (INS) and Multimedia Event Detection task (MED) in TRECVID 2014.

    Otros autores

  • ORAND Team: Instance Search and Multimedia Event Detection Using k-NN Searches

    TRECVID, NIST

    This report describes the participation of the ORAND team at Instance Search task (INS) and Multimedia Event Detection task (MED) in TRECVID 2013.

    Otros autores
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  • Dual Vector Domain Description for Imbalanced Classification

    Springer Berlin Heidelberg

    As machine learning acquires special attention for real-world problem solving, a growing number of new problems not previously considered have appeared. One of such problems is the imbalance in class distributions, which is said to hinder the performance of traditional error-minimization-based classification algorithms. In this paper we propose an improved rule-based decision boundary for the Vector Domain Description that uses an additional nested classification unit to improve the…

    As machine learning acquires special attention for real-world problem solving, a growing number of new problems not previously considered have appeared. One of such problems is the imbalance in class distributions, which is said to hinder the performance of traditional error-minimization-based classification algorithms. In this paper we propose an improved rule-based decision boundary for the Vector Domain Description that uses an additional nested classification unit to improve the accuracy of the outlier class, hence improving the overall performance of the classifier. Computer simulations show that the proposed strategy, which we have termed Dual Vector Domain Description, outperforms related literature approaches in several benchmark instances.

    Otros autores
    • Héctor Allende
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  • Robust Asymmetric Adaboost

    Springer Berlin Heidelberg

    In real world pattern recognition problems, such as computer-assisted medical diagnosis, events of a given phenomena are usually found in minority, making it necessary to build algorithms that emphasize the effect of one of the classes at training time. In this paper we propose a variation of the well-known Adaboost algorithm that is able to improve its performance by using an asymmetric and robust cost function. We assess the performance of the proposed method on two medical datasets and…

    In real world pattern recognition problems, such as computer-assisted medical diagnosis, events of a given phenomena are usually found in minority, making it necessary to build algorithms that emphasize the effect of one of the classes at training time. In this paper we propose a variation of the well-known Adaboost algorithm that is able to improve its performance by using an asymmetric and robust cost function. We assess the performance of the proposed method on two medical datasets and synthetic datasets with different levels of imbalance and compare our results against three state-of-the-art ensemble learning approaches, achieving better and comparable results.

    Otros autores
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  • Neuro-fuzzy-based Arrhythmia Classification Using Heart Rate Variability Features

    XXIX Conferencia Internacional de la Sociedad Chilena de Ciencia de la Computación

    Arrhythmia diagnosis is commonly conducted through visual analysis of human electrocardiograms; a very resource consuming task for physicians. In this paper we present a computational approach for arrhythmia detection based on heart rate variability signal analysis and the application of a neuro-fuzzy classification model called SONFIS. The aforementioned method generates a set of linguistically interpretable inference rules for pattern classification and outperforms artificial neural networks…

    Arrhythmia diagnosis is commonly conducted through visual analysis of human electrocardiograms; a very resource consuming task for physicians. In this paper we present a computational approach for arrhythmia detection based on heart rate variability signal analysis and the application of a neuro-fuzzy classification model called SONFIS. The aforementioned method generates a set of linguistically interpretable inference rules for pattern classification and outperforms artificial neural networks and vector machines in accuracy and several other performance indicators.

    Otros autores
    • Héctor Allende-Cid
    • Alejandro Veloz
    • Héctor Allende
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Reconocimientos y premios

  • AngelHack Global Hackathon Series: Santiago

    AngelHack

    Winner of one of the challenges at AngelHack Global Hackathon Series: Santiago

  • Scientific Initiation Scholarship

    Universidad Técnica Federico Santa María

    Awarded by Dirección General de Investigación y Postgrado at Universidad Técnica Federico Santa María to outstanding postgraduate students

  • Colegio de Ingenieros de Chile A.G. Award

    Colegio de Ingenieros de Chile A.G

    Best performing graduated Computer Engineer at Universidad Técnica Federico Santa María in 2011

  • Federico Santa María Carrera Award

    Universidad Técnica Federico Santa María

    Computer Science student with best performance at Universidad Técnica Federico Santa María in 2011

  • Master’s Degree Scholarship

    Universidad Técnica Federico Santa María

    Scholarship for Master’s Degree studies funding awarded by Dirección General de Investigación y Postgrado at Universidad Técnica Federico Santa María, 2010-2012

  • People’s Choice App at XVII Feria de Software

    Universidad Técnica Federico Santa María

    Awarded to the most voted software product at XVII Feria de Software in 2008

  • In Dean’s List

    Universidad Técnica Federico Santa María

    In Dean’s List at Universidad Técnica Federico Santa María for obtaining outstanding qualifications

Idiomas

  • Spanish

    Competencia bilingüe o nativa

  • English

    Competencia profesional completa

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