References
- 김동규, 이동욱, 박장원, 오성우, 권성준, 이인용, & 최동원. (2022). KB-BERT: 금융 특화 한국어 사전학습 언어모델과 그 응용. 지능정보연구, 28(2), 191-206 https://doi.org/10.13088/JIIS.2022.28.2.191
- 김무성, & 김남규. (2021). 다중 레이블 분류의 정확도 향상을 위한 스킵 연결 오토인코더 기반 레이블 임베딩 방법론. 지능정보연구, 27(3), 175-197. https://doi.org/10.13088/JIIS.2021.27.3.175
- 신병진, 이종훈, 한상진, & 박충식. (2021). ICT 인프라 이상탐지를 위한 조건부 멀티모달 오토인코더에 관한 연구. 지능정보연구, 27(3), 57-73. https://doi.org/10.13088/JIIS.2021.27.3.057
- 유태우, 김윤욱, 정하민, 유현수, & 안용학. (2018). 효율적인 사물 이미지 분류를 위한 계층적 이미지 분류 체계의 설계 및 구현. 융합보안논문지, 18(3), 53-59.
- 이상아, & 신효필. (2020). 감정 분석을 위한 BERT 사전학습모델과 추가 자질 모델의 결합. 한국정보과학회 학술발표논문집, 275-277.
- 임소라, & 권용진. (2017). 특허문서 필드의 기능 적 특성을 활용한 IPC 다중 레이블 분류. 인터넷정보학회논문지, 18(1), 77-88. https://doi.org/10.7472/JKSII.2017.18.1.77
- 한국과학기술정보연구원. (2021, 09.08). 국내 논문 전문 텍스트 데이터셋. 한국과학기술정보연구원. https://doi.org/10.23057/38.
- 한국과학기술정보연구원. (2022, 04.04). 논문 연구분야 분류 데이터. 한국과학기술정보연구원. https://doi.org/10.23057/50.
- Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2006). Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19.
- Cai, L., & Hofmann, T. (2004). Hierarchical document categorization with support vector machines. In Proceedings of the thirteenth ACM international conference on Information and knowledge management, 78-87.
- Chen, Y., Wang, Y., Gu, Y., He, X., Ghamisi, P., & Jia, X. (2019). Deep learning ensemble for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(6), 1882-1897. https://doi.org/10.1109/JSTARS.2019.2915259
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171-4186
- Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J., & Heng, P. A. (2016). 3D deeply supervised network for automatic liver segmentation from CT volumes. International conference on medical image computing and computerassisted intervention, 149-157.
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 580-587.
- Kaikhah, K. (2004). Automatic text summarization with neural networks. 2004 2nd International IEEE Conference on'Intelligent Systems'. Proceedings (IEEE Cat. No. 04EX791), 1, 40-44.
- Le, L., Patterson, A., & White, M. (2018). Supervised autoencoders: improving generalization performance with unsupervised regularizers. Advances in neural information processing systems, 31.
- Lee, C. Y., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2015). Deeply-supervised nets. Artificial intelligence and statistics, 562-570.
- Li, R., Wang, X., Huang, G., Yang, W., Zhang, K., Gu, X., Tran, S. N., Garg, S., Alty, J., & Bai, Q. (2022). A comprehensive review on deep supervision: theories and applications. arXiv preprint arXiv:2207.02376.
- Mishra, D., Chaudhury, S., Sarkar, M., & Soin, A. S. (2018). Ultrasound image segmentation: a deeply supervised network with attention to boundaries. IEEE Transactions on Biomedical Engineering, 66(6), 1637-1648.
- Parida, S., Villatoro-Tello, E., Kumar, S., Motlicek, P., & Zhan, Q. (2020). Idiap submission to swiss-german language detection shared task. SwissText/KONVENS.
- Pereira, G. T., Santos, B. Z., & Cerri, R. (2018). A genetic algorithm for transposable elements hierarchical classification rule induction. 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8.
- Phyu, T. N. (2009). Survey of classification techniques in data mining. Proceedings of the international multiconference of engineers and computer scientists, 1(5).
- Romero, M., Finke, J., & Rocha, C. (2022). A top-down supervised learning approach to hierarchical multi-label classification in networks. Applied Network Science, 7(1), 1-17. https://doi.org/10.1007/s41109-021-00435-x
- Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Parallel distributed processing: explorations in the microstructure of cognition. Cambridge, MA, USA: MIT Press.
- Secker, A. D., Davies, M. N., Freitas, A. A., Timmis, J., Mendao, M., & Flower, D. R. (2007). An experimental comparison of classification algorithms for hierarchical prediction of protein function. Expert Update (Magazine of the British Computer Society's Specialist Group on AI), 9(3), 17-22.
- Shen, Z., Liu, Z., Li, J., Jiang, Y. G., Chen, Y., & Xue, X. (2017). Dsod: learning deeply supervised object detectors from scratch. Proceedings of the IEEE international conference on computer vision, 1919-1927.
- Silla, C. N., & Freitas, A. A. (2011). A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery, 22(1), 31-72. https://doi.org/10.1007/s10618-010-0175-9
- Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27.
- Ullah, M. A., Marium, S. M., Begum, S. A., & Dipa, N. S. (2020). An algorithm and method for sentiment analysis using the text and emoticon. ICT Express, 6(4), 357-360. https://doi.org/10.1016/j.icte.2020.07.003
- Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. (2008). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th international conference on Machine learning, 1096-1103.
- Vlasenko, B., Prasad, R., & Magimai.-Doss, M. (2021). Fusion of acoustic and linguistic information using supervised autoencoder for improved emotion recognition. Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge, 51-59.
- Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 11, 1-8.
- Zhang, G. P. (2000). Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30(4), 451-462. https://doi.org/10.1109/5326.897072
- Zhu, X., & Bain, M. (2017). B-CNN: branch convolutional neural network for hierarchical classification. arXiv preprint arXiv:1709.09890.