{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:15:52Z","timestamp":1760890552145,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031624940"},{"type":"electronic","value":"9783031624957"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-62495-7_2","type":"book-chapter","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:19:24Z","timestamp":1719001164000},"page":"17-27","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Approach to\u00a0Predict Optimal Configurations for\u00a0LDA-Based Topic Modeling"],"prefix":"10.1007","author":[{"given":"Mou","family":"Saha","sequence":"first","affiliation":[]},{"given":"Doina","family":"Logof\u0103tu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Treude,C., Wagner, M.: Predicting good configurations for github and stack overflow topic models. In: 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR), Montreal, QC, Canada, pp. 84\u201387 (2019). https:\/\/doi.org\/10.1109\/MSR.2019.00022","DOI":"10.1109\/MSR.2019.00022"},{"issue":"Jan","key":"2_CR2","first-page":"993","volume":"3","author":"M Blei","year":"2003","unstructured":"Blei, M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"issue":"suppl 1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"5228","DOI":"10.1073\/pnas.0307752101","volume":"101","author":"TL Griffiths","year":"2004","unstructured":"Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(suppl 1), 5228\u20135235 (2004)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Wallach, H.M., Murray, I., Salakhutdinov, R., Mimno, D.: Evaluation methods for topic models. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1105\u20131112 (2009)","DOI":"10.1145\/1553374.1553515"},{"key":"2_CR5","unstructured":"Bardenet, R., Brendel, M., K\u00e9gl, B., Sebag, M.: Collaborative hyperparameter tuning. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 199\u2013207 (2013)"},{"key":"2_CR6","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951\u20132959 (2012)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 937\u2013946 (2009)","DOI":"10.1145\/1557019.1557121"},{"issue":"Feb","key":"2_CR8","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"2_CR9","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2018","unstructured":"Jelodar, H., et al.: Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed. Tools Appl. 78(11), 15169\u201315211 (2018). https:\/\/doi.org\/10.1007\/s11042-018-6894-4","journal-title":"Multimed. Tools Appl."},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Sasi, S., Lilywala, T.Y., Bhattacharya, B.S.: Optimising hyperparameter search in a visual thalamocortical pathway model. In: 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, pp. 1\u20138 (2022). https:\/\/doi.org\/10.1109\/IJCNN55064.2022.9892380","DOI":"10.1109\/IJCNN55064.2022.9892380"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. arXiv (2012). https:\/\/doi.org\/10.48550\/ARXIV.1205.2662","DOI":"10.48550\/ARXIV.1205.2662"},{"key":"2_CR12","unstructured":"Hughes, M.: Reliable and scalable variational inference for the hierarchical Dirichlet process. In: Artificial Intelligence and Statistics. PMLR (2015). proceedings.mlr.press\/v38\/hughes15.html"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"106411","DOI":"10.1016\/j.infsof.2020.106411","volume":"130","author":"A Panichella","year":"2021","unstructured":"Panichella, A.: A systematic comparison of search-based approaches for LDA hyperparameter tuning. Inf. Softw. Technol. 130, 106411 (2021). https:\/\/doi.org\/10.1016\/j.infsof.2020.106411","journal-title":"Inf. Softw. Technol."},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Panichella, A., Dit, B., Oliveto, R., Di Penta, M., Poshynanyk, D., De Lucia, A.: How to effectively use topic models for software engineering tasks? An approach based on genetic algorithms. In: 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, pp. 522\u2013531 (2013). https:\/\/doi.org\/10.1109\/ICSE.2013.6606598","DOI":"10.1109\/ICSE.2013.6606598"},{"issue":"476","key":"2_CR15","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1198\/016214506000000302","volume":"101","author":"YW Teh","year":"2006","unstructured":"Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical Dirichlet Processes. J. Am. Stat. Assoc. 101(476), 1566\u20131581 (2006). https:\/\/doi.org\/10.1198\/016214506000000302","journal-title":"J. Am. Stat. Assoc."},{"key":"2_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-642-13657-3_43","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"R Arun","year":"2010","unstructured":"Arun, R., Suresh, V., Veni Madhavan, C.E., Narasimha Murthy, M.N.: On finding the natural number of topics with latent Dirichlet allocation: some observations. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6118, pp. 391\u2013402. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13657-3_43"},{"issue":"10","key":"2_CR17","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1002\/smr.1802","volume":"28","author":"D Binkley","year":"2016","unstructured":"Binkley, D., Heinz, D., Lawrie, D., Overfelt, J.: Source code analysis with LDA. J. Softw.: Evol. Process 28(10), 893\u2013920 (2016). https:\/\/doi.org\/10.1002\/smr.1802","journal-title":"J. Softw.: Evol. Process"}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62495-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:19:58Z","timestamp":1719001198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62495-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031624940","9783031624957"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62495-7_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}