Effectiveness of active forgetting in machine learning applied to financial problems

• Autorzy: Nakayama H. , Yoshii K.
• Języki publikacji: EN

Abstrakty

EN

One of main features in financial investment problems is that the situation changes very often over time. Under this circumstance, in particular, it has been observed that additional learning plays an effective role. However, since the rule for classification becomes more and more complex with only additional learning, some appropriate forgetting is also necessary. It seemms natural that many data are forgotten as the time elapses. On the other hand, it is expected more effective to forget unnecessary data actively. In this paper, several methods for active forgetting are suggested. The effectiveness of active forgetting is shown by examples in stock portfolio problems.

Słowa kluczowe

EN

pattern classification; potential method; additional learning; forgetting

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2002
• Tom: nr 3
• Strony: 24--29
• Opis fizyczny: Bibliogr. 8 poz., tab., rys.

Twórcy

autor

Nakayama H.

autor

Yoshii K.

• Department of Information Science and Systems Engineering, Konan University, Japan,