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Wyszukujesz frazę "vector optimization" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
The Learning System by the Least Squares Support Vector Machine Method and its Application in Medicine
Autorzy:
Szewczyk, P.
Baszun, M.
Powiązania:
https://bibliotekanauki.pl/articles/307897.pdf
Data publikacji:
2011
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
classification
Grid-Search
particle swarm optimization (PSO)
patients diagnosis
support vector machine (SVM)
Opis:
In the paper it has been presented the possibility of using the least squares support vector machine to the initial diagnosis of patients. In order to find some optimal parameters making the work of the algorithm more detailed, the following techniques have been used: K-fold Cross Validation, Grid-Search, Particle Swarm Optimization. The result of the classification has been checked by some labels assigned by an expert. The created system has been tested on the artificially made data and the data taken from the real database. The results of the computer simulations have been presented in two forms: numerical and graphic. All the algorithms have been implemented in the C# language.
Źródło:
Journal of Telecommunications and Information Technology; 2011, 3; 109-113
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Solving Support Vector Machine with Many Examples
Autorzy:
Białoń, P.
Powiązania:
https://bibliotekanauki.pl/articles/308497.pdf
Data publikacji:
2010
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
concept drift
convex optimization
data mining
network failure detection
stream processing
support vector machines
Opis:
Various methods of dealing with linear support vector machine (SVM) problems with a large number of examples are presented and compared. The author believes that some interesting conclusions from this critical analysis applies to many new optimization problems and indicates in which direction the science of optimization will branch in the future. This direction is driven by the automatic collection of large data to be analyzed, and is most visible in telecommunications. A stream SVM approach is proposed, in which the data substantially exceeds the available fast random access memory (RAM) due to a large number of examples. Formally, the use of RAM is constant in the number of examples (though usually it depends on the dimensionality of the examples space). It builds an inexact polynomial model of the problem. Another author's approach is exact. It also uses a constant amount of RAM but also auxiliary disk files, that can be long but are smartly accessed. This approach bases on the cutting plane method, similarly as Joachims' method (which, however, relies on early finishing the optimization).
Źródło:
Journal of Telecommunications and Information Technology; 2010, 3; 65-70
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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