Autor: Kamimura, R. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Accumulative information enhancement in the self-organizing maps and its application to the analysis of mission statements
Autorzy:: Kitajima, R.
Kamimura, R.
Powiązania:: https://bibliotekanauki.pl/articles/91614.pdf
Data publikacji:: 2015
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: information
neural systems
computational method
artificial data set
mission
statement
informacja
systemy neuronowe
metoda obliczeniowa
zbiór danych
misja
komunikat
Opis:: This paper proposes a new information-theoretic method based on the information enhancement method to extract important input variables. The information enhancement method was developed to detect important components in neural systems. Previous methods have focused on the detection of only the most important components, and therefore have failed to fully incorporated the information contained in the components into learning processes. In addition, it has been observed that the information enhancement method cannot always extract input information from input patterns. Thus, in this paper a computational method is developed to accumulate information content in the process of information enhancement. The method was applied to an artificial data set and the analysis of mission statements. The results demonstrate that while we were able to explicitly extract the symmetric properties of the data from the artificial data set, only one main factor was able to be extracted from the mission statement, namely, “contribution to the society”. The companies with higher profits tend to have mission statements concerning the society. The results can be considered to be a first step toward the full clarification of the importance of mission statements in actual business activities.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 3; 161-176
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Self-assimilation for solving excessive information acquisition in potential learning
Autorzy:: Kamimura, R.
Kitago, T.
Powiązania:: https://bibliotekanauki.pl/articles/91776.pdf
Data publikacji:: 2018
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: neural networks
learning
excessive information acquisition
self-assimilation method
sieci neuronowe
uczenie się
pozyskiwanie informacji
Opis:: The present paper aims to propose a new computational method for potential learning to improve generalization and interpretation. Potential learning has been proposed to simplify the computational procedures of information maximization and to specify which neurons should be fired. However, it is often the case that potential learning sometimes absorbs too much information content on input patterns in the early stage of learning, which tends to degrade generalization performance. This can be solved by making potential learning as slow as possible. Accordingly, we here propose a procedure called “self-assimilation” in which connection weights are accentuated by their characteristics observed in the specific learning step. This makes it possible to predict future connection weights in the early stage of learning. Thus, it is possible to improve generalization by slow learning and at the same time to improve the interpretation of connection weights via the enhanced characteristics of the connection weights. The method was applied to an artificial data set, as well as a real data set of counter services at a local government office in the Tokyo metropolitan area. The results show that improved generalization was observed by making learning as slow as possible. In addition, the number of strong connection weights became smaller for better interpretation by self-assimilation.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 1; 5-29
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "Kamimura, R." wg kryterium: Autor

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język