Titlebook: Similarity Search and Applications; 6th International Co Nieves Brisaboa,Oscar Pedreira,Pavel Zezula Conference proceedings 2013 Springer-V

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Karina Figueroa,Rodrigo Paredesamework: learning with interdependent data..Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is 978-3-319-35390-6978-3-319-15726-9

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Oscar García-Olalla,Enrique Alegre,María Teresa García-Ordás,Laura Fernández-Roblesnges.Reimagining and rapid transition of learning.Interested readership includes policymakers, academics, educators, researchers in pedagogy and learning theory, schoolteachers, learning industry, further and c978-3-031-04285-0978-3-031-04286-7Series ISSN 2367-3370 Series E-ISSN 2367-3389

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A New Concept of Sets to Handle Similarity in Databases: The SimSetsg. Specifically, our main contributions are: (i) highlighting the central properties of SimSets; (ii) proposing the basic algorithms required to create them from metric datasets, which were carefully designed to be naturally embedded into existing DBMS, and; (iii) evaluating their use on real world

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Machine Learning for Image Classification and Clustering Using a Universal Distance Measure Based on a collection of such cases any supervised or unsupervised learning algorithm can be used to train and produce an image classifier or image cluster analysis. In this paper we present the image feature-extraction method and use it on several supervised and unsupervised learning experiments f

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Evaluation of LBP Variants Using Several Metrics and kNN Classifierste using ALBPS and Chi Square distance, outperforming the ALBP in 1,07% and the original LBP in 6,76%. In relation to the binary sperm dataset, the best result was obtained with ALBPS and a kNN classifier (k=9), reaching a 72.66% of hit rate using the Chi Square metric, outperforming the original LB

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