Machine Learning and Applications

Group description

The field of Automated Learning (Machine Learning) is a central part of the new technological revolution based on the intelligent use of information. Traditionally, the main problems that are investigated in this area are those of pattern recognition or classification, function approximation or regression of continuous variables, and the discovery of hidden structures in data or Clustering. Logically, the development of new methods and algorithms are focused at first on the most simple and typical ones to find, for example in stationary problems in time, with an abundance of examples to learn from and with only a few classes fairly balanced between them. However, new types of data from genomics, proteomics and continuous monitoring equipment of critical systems have introduced new challenges in Automated Learning. This project proposes the development of new methods, or the extension of existing techniques when appropriate, to efficiently model this new kind of data, including unsteady regression classification problems or with great noise, clustering classification problems with an extremely high number of input variables or classification problems with a significant imbalance between classes. All lines of this project include applications to current issues of great technological interest, covering areas of biotechnology, agriculture and steel industries.

investigacion_grupo_integrantes

 
 
 
 
Persona

Dr. Pablo Granitto

Investigador Principal de CONICET y Profesor Titular de UNR

Investigate in Aprendizaje automatizado (machine learning). Métodos de ensemble. Selección de características. Clustering.

investigacion_mas_sobre Pablo
 
 
 
Persona

Dr. Ariel Bayá

Investigador Adjunto de CONICET

investigacion_mas_sobre Ariel
Persona

Dr. Guillermo Grinblat

Investigador Adjunto de CONICET (en licencia)

investigacion_mas_sobre Guillermo
Persona

Dra. Mónica Larese

Investigadora Adjunta de CONICET y JTP UNR

investigacion_mas_sobre Mónica
Persona

Lic. Joaquín Mesuro

Becario Doctoral - CONICET

investigacion_mas_sobre Joaquín
Persona

Dra. María Eugenia Torio

investigacion_mas_sobre María Eugenia
Persona

Dr. Lucas Uzal

Investigador Adjunto de CONICET y Profesor Adjunto de UNR (en licencia)

investigacion_mas_sobre Lucas
Persona

Dr. Pablo Verdes

Investigador Adjunto de CONICET (en licencia)

investigacion_mas_sobre Pablo

investigacion_grupo_proyectos

 
investigacion_grupo_nombre_proyecto: proyecto 1 granitto
investigacion_grupo_nombre_proyecto: proyecto 2 granitto
investigacion_grupo_nombre_proyecto: proyecto 3 granitto

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investigacion_grupo_produccion

 
  • G.L. GRINBLAT; P. M. GRANITTO; H. A. CECCATTO. Time-adaptive Support Vector Machines. - 2008
  • E. ECCEL; L. GHIELMI; P. M. GRANITTO; R. BARBIERO; D. CESARI. TECNICHE DI POST-ELABORAZIONE DI PREVISIONE DI TEMPERATURA MINIMA A CONFRONTO PER UN'AREA ALPINA. - 2008
  • A.E. BAYÁ; P.M. GRANITTO. ISOMAP Based Metrics for Clustering. - 2008
  • P. M. GRANITTO; F. BIASIOLI; C. FURLANELLO; F. GASPERI. Efficient feature selection for PTR-MS fingerprinting of agroindustrial products. - 2008
  • A. E. BAYÁ; P. M. GRANITTO. Clustering gene expression data with the PKNNG metric. - 2008
  • AHUMADA, HERNÁN. GRINBLAT, GUILLERMO. UZAL, LUCAS. GRANITTO, PABLO. CECCATTO, ALEJANDRO. Coupling REPMAC with FDA to solve highly imbalanced classification problems. - 2008
  • LARESE, MÓNICA G.; GÓMEZ, JUAN C.. Accurate automatic spot addressing for microarray images. - 2008
  • LARESE, MÓNICA G.; GÓMEZ, JUAN C.. Fully automatic procedure for accurate spot addressing in microarray images. - 2008