Now showing items 1-10 of 28
Hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification
Usually, image classification methods have supervised or unsupervised learning paradigms. While unsupervised methods do not need training data, the meanings behind the classified elements are not explicitly know. Conversely, ...
Hierarchical density estimates for data clustering, visualization, and outlier detection
(ACMNew York, 2015-07)
An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level ...
An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning.
(Association for Computing Machinery - ACMNew York, 2014-08)
Unsupervised models can provide supplementary soft constraints to help classify new “target” data because similar instances in the target set are more likely to share the same class label. Such models can also help detect ...
Agrupamento de dados semissupervisionado na geração de regras fuzzy
(Universidade Federal de São CarlosUFSCarPrograma de Pós-Graduação em Ciência da Computação - PPGCCCâmpus São Carlos, 2010-08-27)
Inductive learning is, traditionally, categorized as supervised and unsupervised. In supervised learning, the learning method is given a labeled data set (classes of data are known). Those data sets are adequate for ...
Semi-supervised feature selection
(Universidade Federal de Minas GeraisUFMG, 2013-03-22)
As data acquisition has become relatively easy and inexpensive, datasets are becoming extremely large, both in relation to the numberof variables, and on the number of instances. However, the same isnot true for labeled ...
Algoritmos particionais semissupervisionados com ponderação automática de variáveis
(UNIVERSIDADE FEDERAL DE PERNAMBUCOUFPEBrasilPrograma de Pos Graduacao em Ciencia da Computacao, 2016)
Semi-Supervised Self-Organizing Maps with Time-Varying Structures for Clustering and Classification
(Universidade Federal de PernambucoUFPEBrasilPrograma de Pos Graduacao em Ciencia da Computacao, 2019)
Open issues for partitioning clustering methods: an overview
(John Wiley & SonsOxford, 2014-05)
Over the last decades, a great variety of data mining techniques have been developed to reach goals concerning Knowledge Discovery in Databases. Among them, cluster detection techniques are of major importance. Although ...