A meta-learning network method for few-shot multi-class classification problems with numerical data

Autor:	Lang Wu
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Multi-class classification problem Few-shot problem Meta-learning Convolutional neural network Electronic computers. Computer science QA75.5-76.95 Information technology T58.5-58.64
Zdroj:	Complex & Intelligent Systems, Vol 10, Iss 2, Pp 2639-2652 (2023)
Druh dokumentu:	article
ISSN:	2199-4536 2198-6053
DOI:	10.1007/s40747-023-01281-3
Popis:	Abstract The support vector machine (SVM) method is an important basis of the current popular multi-class classification (MCC) methods and requires a sufficient number of samples. In the case of a limited number of samples, the problem of over-learning easily occurs, resulting in unsatisfactory classification. Therefore, this work investigates an MCC method that requires only a small number of samples. During model construction, raw data are converted into two-dimensional form via preprocessing. Via feature extraction, the learning network is measured and the loss function minimization principle is considered to better solve the problem of learning based on a small sample. Finally, three examples are provided to illustrate the feasibility and effectiveness of the proposed method.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/559b4adad5034ddc91ff082d838f3d67 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ