@inproceedings{ce7332ef9804466d8236627b77681cd9,
title = "Multi-codebook Fuzzy Neural Network Using Incremental Learning for Multimodal Data Classification",
abstract = "One of the challenge in classification is classification in multimodal data. This paper proposed multi-codebook fuzzy neural network by using incremental learning for multimodal data classification. There are 2 variations of the proposed method, one uses a static threshold, and the other uses a dynamic threshold. Based on the experiment result, the multicodebook FNGLVQ using dynamic incremental learning has the highest improvement compared to the original FNGLVQ. It achieves 15.65% margin in synthetic dataset, 5.02 % margin in benchmark dataset, and 11.30% on average all dataset.",
keywords = "classification, fuzzy, multi-codebook, multimodal, neural network",
author = "Ma'sum, {Muhammad Anwar} and Wisnu Jatmiko",
year = "2019",
month = jul,
doi = "10.1109/ACIRS.2019.8935971",
language = "English",
series = "2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "205--210",
booktitle = "2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019",
address = "United States",
note = "4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019 ; Conference date: 13-07-2019 Through 15-07-2019",
}