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Seminario InCo Víctor Codocedo (INRIA Chile, Chile) We introduce a method for sampling formal concepts using locality sensitive hashing (LSH). LSH is a technique used for finding approximate nearest neighbours given a set of hashing functions.Through our approach, we are able to predict the probability of an extent in the concept lattice given set of objects and their similarity index, a generalization of the Jaccard similarity between sets.Our approach allows defining a lattice-based amplification construction to design arbitrarily discriminative sampling settings.

Lecciones de un proyecto de Ciencia de Datos con datos reales: Mecánica Predictiva

By en seminario

September 13, 2017

Seminario ICT4V Víctor Codocedo (INRIA Chile, Chile) Inria Chile trabaja actualmente con Tracktec, una empresa dedicada al monitoreo telemétrico de variables mecánicas en buses y camiones en distintas aplicaciones del transporte. Inicialmente pensado como un proyecto para predicción de fallas en buses, la ejecución del mismo ha mostrado la complejidad del problema y la insuficiencia de los datos, incluso cuando su volumen excede los 1000 millones de lecturas para cuatro meses de registro.

Workshop en Knowledge Discovery y Cyberseguridad

By Felipe Zipitría in seminar

April 20, 2016

Program 09:00 - Formal Concept Analysis (FCA) - Basics and Beyond Amedeo Napoli (INRIA Nancy, France) Knowledge discovery in large and complex datasets is one of the main topics addressed by the so-called Data Science but is also a topic of main interest for the Science of Knowledge (or Artificial Intelligence). Indeed data and knowledge are interacting and knowledge discovery is applied on datasets and has a direct impact on the design of knowledge bases (or ontologies).

On Locality Sensitive Hashing for Sampling Extent Generators

Lecciones de un proyecto de Ciencia de Datos con datos reales: Mecánica Predictiva

Workshop en Knowledge Discovery y Cyberseguridad

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