{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:18:47Z","timestamp":1743085127581,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981311"},{"type":"electronic","value":"9789819981328"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8132-8_35","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"465-475","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mutually Guided Dendritic Neural Models"],"prefix":"10.1007","author":[{"given":"Yanzi","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergey","family":"Ablameyko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Adedigba, A.P., Adeshina, S.A., Aibinu, A.M.: Performance evaluation of deep learning models on mammogram classification using small dataset. Bioengineering 9(4), 161 (2022)","DOI":"10.3390\/bioengineering9040161"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Bansal, A., Singhrova, A.: Performance analysis of supervised machine learning algorithms for diabetes and breast cancer dataset. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 137\u2013143. IEEE (2021)","DOI":"10.1109\/ICAIS50930.2021.9396043"},{"key":"35_CR3","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"35_CR4","first-page":"1","volume":"13","author":"MA Chandra","year":"2021","unstructured":"Chandra, M.A., Bedi, S.: Survey on SVM and their application in image classification. Int. J. Inf. Technol. 13, 1\u201311 (2021)","journal-title":"Int. J. Inf. Technol."},{"key":"35_CR5","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neunet.2022.03.004","volume":"150","author":"X Gong","year":"2022","unstructured":"Gong, X., Yu, L., Wang, J., Zhang, K., Bai, X., Pal, N.R.: Unsupervised feature selection via adaptive autoencoder with redundancy control. Neural Netw. 150, 87\u2013101 (2022)","journal-title":"Neural Netw."},{"key":"35_CR6","unstructured":"Han, B., et al.: Co-teaching: Robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems 31 (2018)"},{"issue":"7","key":"35_CR7","doi-asserted-by":"publisher","first-page":"4162","DOI":"10.1109\/TCYB.2022.3141380","volume":"53","author":"J Ji","year":"2023","unstructured":"Ji, J., Dong, M., Lin, Q., Tan, K.C.: Noninvasive cuffless blood pressure estimation with dendritic neural regression. IEEE Trans. Cybern. 53(7), 4162\u20134174 (2023). https:\/\/doi.org\/10.1109\/TCYB.2022.3141380","journal-title":"IEEE Trans. Cybern."},{"key":"35_CR8","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1016\/j.neucom.2015.09.052","volume":"173","author":"J Ji","year":"2016","unstructured":"Ji, J., Gao, S., Cheng, J., Tang, Z., Todo, Y.: An approximate logic neuron model with a dendritic structure. Neurocomputing 173, 1775\u20131783 (2016)","journal-title":"Neurocomputing"},{"key":"35_CR9","unstructured":"Li, J., Socher, R., Hoi, S.C.: Dividemix: learning with noisy labels as semi-supervised learning. arXiv:2002.07394 (2020)"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, C.: Distributed semi-supervised learning with positive and unlabeled data. In: 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pp. 488\u2013492. IEEE (2022)","DOI":"10.1109\/ICBAIE56435.2022.9985903"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Ning, X., et al.: A review of research on co-training. Concurrency and computation: practice and experience p. e6276 (2021)","DOI":"10.1002\/cpe.6276"},{"key":"35_CR12","doi-asserted-by":"publisher","unstructured":"Saravanan, R., Sujatha, P.: A state of art techniques on machine learning algorithms: A perspective of supervised learning approaches in data classification. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 945\u2013949 (2018). https:\/\/doi.org\/10.1109\/ICCONS.2018.8663155","DOI":"10.1109\/ICCONS.2018.8663155"},{"key":"35_CR13","doi-asserted-by":"publisher","unstructured":"Suzuki, K.: Small data deep learning for lung cancer detection in ct. In: 2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 114\u2013118 (2022). https:\/\/doi.org\/10.1109\/BigDataService55688.2022.00025","DOI":"10.1109\/BigDataService55688.2022.00025"},{"key":"35_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3092351","volume":"60","author":"X Tai","year":"2021","unstructured":"Tai, X., Li, M., Xiang, M., Ren, P.: A mutual guide framework for training hyperspectral image classifiers with small data. IEEE Trans. Geosci. Remote Sens. 60, 1\u201317 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Tang, Y., Ji, J., Zhu, Y., Gao, S., Tang, Z., Todo, Y., et al.: A differential evolution-oriented pruning neural network model for bankruptcy prediction. Complexity 2019 (2019)","DOI":"10.1155\/2019\/8682124"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Tang, Y., Song, Z., Zhu, Y., Hou, M., Tang, C., Ji, J.: Adopting a dendritic neural model for predicting stock price index movement. Expert Syst. Appl. 205, 117637 (2022)","DOI":"10.1016\/j.eswa.2022.117637"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Wang, L., Xu, Y., Xu, H., Liu, J., Wang, Z., Huang, L.: Enhancing federated learning with in-cloud unlabeled data. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 136\u2013149. IEEE (2022)","DOI":"10.1109\/ICDE53745.2022.00015"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Hyperspectral image classification based on dense pyramidal convolution and multi-feature fusion. Remote Sens. 15(12), 2990 (2023)","DOI":"10.3390\/rs15122990"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8132-8_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:55:07Z","timestamp":1710258907000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8132-8_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981311","9789819981328"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8132-8_35","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}