A visual approach for support to multi-instances learning

• Grant number: 13/25055-2
• Support Opportunities: Scholarships in Brazil - Master
• Start date: May 01, 2014
• End date: April 30, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Rosane Minghim
• Grantee: Sonia Castelo Quispe
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications, AP.TEM

Abstract

Information visualization involves using interactive visual interfaces to clearly represent the information content from a data set for the end user. With the maturity of the area, new and improved algorithms are available to visually support the data analysis. The learning processes guided by the user, in particular, can benefit greatly from the use of Visualization. Recently an approach has been developed to support visual classification tasks and applied to the analysis of various types of data, such as texts, images and biological data. However, this approach does not adequately encompasses some important situations, such as handling large datasets and multiple instance learning, the latter being an important model in many applications. This project aims at developing approaches to contribute to the scalability of visual classification approach and tailor the method for multi-instance classification processes.

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.)

CASTELO, SONIA; PONTI, MOACIR; MINGHIM, ROSANE. A Visual Mining Approach to Improved Multiple- Instance Learning. ALGORITHMS, v. 14, n. 12, DEC 2021. (19/07316-0, 13/25055-2)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

QUISPE, Sonia Castelo. A visual approach for support to multi-instances learning. 2015. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.