Distance Learning and Inverse Mapping of Visualizations Applied to Text Mining

• Grant number: 17/08817-7
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: December 01, 2017
• End date: November 30, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Fernando Vieira Paulovich
• Grantee: Gabriel Dias Cantareira
• Supervisor: Evangelos Milios
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: Dalhousie University, Canada
• Associated to the scholarship: 15/08118-6 - Inverse Mapping: Employing Interactive Manipulation to Transform Computational Models, BP.DR

Abstract

With the increasing amount and complexity of data gathered and stored by computer systems, the task of exploring and extracting knowledge from it is becoming a hurdle. Aiming to solve this problem, data mining, machine learning, and information visualization fields have become of great importance. Recently, these fields were combined into a common new field, called Visual Analytics (VA), which comprehends the use of visual representations to control the creation of data mining and machine learning computational models, allowing users to incorporate knowledge into analytical tasks. Amongst the different types of data explored using VA techniques, text is a common and important type, as most of the human knowledge is stored in such format. This internship project proposes the application of the inverse mapping concepts studied in the original doctorate proposal in a text analytics environment, aiming at providing a better understanding of the effect of user-driven similarity metrics in VA text analytics systems.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

CANTAREIRA, GABRIEL D.; PAULOVICH, FERNANDO, V; ETEMAD, ELHAM; KERREN, A; HURTER, C; BRAZ, J. Visualizing Learning Space in Neural Network Hidden Layers. VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP, v. N/A, p. 12-pg., 2020-01-01. (17/08817-7, 15/08118-6)

CANTAREIRA, GABRIEL D.; ETEMAD, ELHAM; PAULOVICH, FERNANDO V.. Exploring Neural Network Hidden Layer Activity Using Vector Fields. INFORMATION, v. 11, n. 9, p. 15-pg., 2020-09-01. (17/08817-7, 15/08118-6)