Lilian Berton
CV Lattes
GoogleMyCitations
ResearcherID
ORCID
Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. Instituto de Ciência e Tecnologia (ICT) (Institutional affiliation for the last research proposal) Birthplace: Brazil
, acting on the following subjects: base de regras fuzzy, conjuntos fuzzy, trypoxylon lactitarse saussure, problema de caminho mínimo and grafos com parâmetros fuzzy. (Source: Lattes Curriculum)
- Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications, AP.R
Abstract
Graph-based semi-supervised learning (SSL) is a promising paradigm for modeling the manifold in multidimensional data space, and is effective in propagating a small number of initial labels to a large amount of unlabelled data. This approach has been used in a variety of applications, such as image segmentation and annotation, human speech recognition, text classification, etc. Recently...
- Label noise reduction in graph-based semi-supervised learning, BP.MS
Abstract
The recent growth of online content on the Web has allowed collecting more user-generated data with noisy and missing labels, e.g., data from social tags and voted labels from Amazon's Mechanical Turks. Most machine learning methods, which require accurate label sets, could not be trusted or have good performance. Graph classification has drawn increasing interests due to the rapid risi...
- Semi-supervised graph-based algorithms for word sense Disambiguation, BP.MS
Abstract
Word Sense Disambiguation (WSD) is an open problem of Natural Language Processing, which aims to identify the appropriate sense of a word in some context. Many approaches have been proposed to solve the problem, such as Knowledge-based, Supervised and Unsupervised Learning. Semi-Supervised Learning (SSL) has recently become an active research area that requires a small amount of labeled...
- Analysis of the relationships between the El Niño phenomenon and climatological variables using complex networks, BP.IC
Abstract
The analysis of climatological data is important to understand the evolution and to develop more precise methods for the prediction of phenomena like El Niño. Different climatological data have been collected over the years in different places around the planet, such as temperature, precipitation, pressure, among others. The analysis of such data sets has traditionally been carried out ...
- Semi-supervised learning based on complex networks and applications: climate data analysis, BP.TT
- Semi-supervised learning based on complex networks and applications: text mining, BP.TT
- Keywords extraction in text using complex networks, BP.IC
Abstract
Keywords are an important way to summarize documents, as well to facilitate a topic search. Only a small fraction of documents have keywords assigned by the author and execute this task manually in existing documents are a very laborious task. Therefore, it is highly desirable to automate the process of extracting keywords. Many methods require a number of labeled texts to train a class...
(Only some records are available in English at this moment)
| 1 / 1 | Ongoing research grants |
| 1 / 1 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 5 / 5 | Completed scholarships in Brazil |
| 10 / 10 | All research grants and scholarships |
| Associated processes | |
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
| Publications | 3 |
| Citations | 3 |
| Cit./Article | 1.0 |
| Data from Web of Science | |
BERTON, LILIAN; FALEIROS, THIAGO DE PAULO; VALEJO, ALAN; VALVERDE-REBAZA, JORGE; LOPES, ALNEU DE ANDRADE. RGCLI: Robust Graph that Considers Labeled Instances for Semi Supervised Learning. Neurocomputing, v. 226, p. 238-248, FEB 22 2017. Web of Science Citations: 3. (11/23689-9, 11/21880-3, 13/12191-5, 15/14228-9)
VEGA-OLIVERO, DIDIER A.; GOMES, PEDRO SPOLJARIC; MILIOS, EVANGELOS E.; BERTON, LILIAN. A multi-centrality index for graph-based keyword extraction. INFORMATION PROCESSING & MANAGEMENT, v. 56, n. 6, NOV 2019. Web of Science Citations: 0. (18/24260-5, 18/01722-3, 15/50122-0, 16/23698-1)
VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Evaluating link prediction by diffusion processes in dynamic networks. SCIENTIFIC REPORTS, v. 9, JUL 25 2019. Web of Science Citations: 0. (18/01722-3, 13/07375-0, 18/24260-5, 15/50122-0, 16/23698-1)
(References retrieved automatically from State of São Paulo Research Institutions)
BERTON, Lilian. Construção de redes baseadas em vizinhança para o aprendizado semissupervisionado. 2016. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos. (11/21880-3)
BERTON, Lilian. Caracterização de classes e detecção de outliers em redes complexa. 2011. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos. (09/02985-9)