Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Priscilla de Abreu Lopes"'
Publikováno v:
EUSFLAT Conf.
Publikováno v:
FUZZ-IEEE
Fuzzy clustering algorithms have recently been investigated as appropriate techniques to extract knowledge from Data Streams due to their unsupervised nature and flexibility to deal with changes in the distribution of data. While most fuzzy clusterin
Publikováno v:
FUZZ-IEEE
In many real-world applications data arrive continuously, in the form of streams. Such data can be used for the acquisition of knowledge by machine learning methods. In data streams learning, novelty detection is a relevant topic, which aims to ident
Publikováno v:
BRACIS
In many real-world applications, data arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. In data streams learning, classification problems aim to predict the true class of incoming instances in real
Publikováno v:
FUZZ-IEEE
Systems capable of generating data quickly and continuously, known as data streams, are a reality today and tend to increase. Due to the nature of data streams, unsupervised learning, such as clustering algorithms, is appropriate. In addition, techni
Autor:
Heloisa A. Camargo, Priscilla de Abreu Lopes, Ivana Yoshie Sumida, Sandra Sandri, Haroldo F. de Campos Velho
Publikováno v:
IFSA-EUSFLAT
In order to to predict regime duration in a given chaotic system, for a set of output prototypes are available, we propose to use a clustering technique for the definition of classes of regime duration, which are then used by a chosen classifier. In
Publikováno v:
IRI
The ease of acquiring great volumes of data and the problems with manual labeling and interpretation of the output of learning results motivate the research on methods that can deal with the inherent aspects of this type of data. Studies suggest that