{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T04:15:04Z","timestamp":1746591304024,"version":"3.40.5"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030461461"},{"type":"electronic","value":"9783030461478"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46147-8_41","type":"book-chapter","created":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T02:03:39Z","timestamp":1588298619000},"page":"681-696","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts"],"prefix":"10.1007","author":[{"given":"Maarten","family":"Grootendorst","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joaquin","family":"Vanschoren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"41_CR1","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297\u20135307 (2016)","DOI":"10.1109\/CVPR.2016.572"},{"key":"41_CR2","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Zisserman, A.: All about VLAD. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1578\u20131585 (2013)","DOI":"10.1109\/CVPR.2013.207"},{"key":"41_CR3","unstructured":"Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings. In: International Conference for Learning Representations (2017)"},{"key":"41_CR4","doi-asserted-by":"crossref","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: Proceedings of the 20th International Conference on Pattern Recognition, pp. 3121\u20133124. IEEE (2010)","DOI":"10.1109\/ICPR.2010.764"},{"key":"41_CR5","unstructured":"Cardoso-Cachopo, A.: Improving methods for single-label text categorization. Ph.D thesis, Instituto Superior Tecnico, Universidade Tecnica de Lisboa (2007)"},{"key":"41_CR6","unstructured":"Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. In: Advances in Neural Information Processing Systems, pp. 3079\u20133087 (2015)"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Delhumeau, J., Gosselin, P.H., J\u00e9gou, H., P\u00e9rez, P.: Revisiting the VLAD image representation. In: Proceedings of the 21st International Conference on Multimedia, pp. 653\u2013656. ACM (2013)","DOI":"10.1145\/2502081.2502171"},{"key":"41_CR8","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Jegou, H., Douze, M., Schmid, C., P\u00e9rez, P.: Aggregating local descriptors into a compact image representation. In: Computer Vision and Pattern Recognition, pp. 3304\u20133311. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5540039"},{"issue":"9","key":"41_CR10","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","volume":"34","author":"H Jegou","year":"2012","unstructured":"Jegou, H., Perronnin, F., Douze, M., S\u00e1nchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. Trans. Pattern Anal. Mach. Intell. 34(9), 1704\u20131716 (2012)","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"key":"41_CR11","unstructured":"Joachims, T.: A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: International Conference on Machine Learning, pp. 143\u2013151 (1996)"},{"key":"41_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/BFb0026683","volume-title":"Machine Learning: ECML-98","author":"T Joachims","year":"1998","unstructured":"Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: N\u00e9dellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137\u2013142. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0026683"},{"key":"41_CR13","volume-title":"Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies","author":"JD Kelleher","year":"2015","unstructured":"Kelleher, J.D., Mac Namee, B., D\u2019Arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press, Cambridge (2015)"},{"key":"41_CR14","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neucom.2017.05.046","volume":"266","author":"HK Kim","year":"2017","unstructured":"Kim, H.K., Kim, H., Cho, S.: Bag-of-concepts: comprehending document representation through clustering words in distributed representation. Neurocomputing 266, 336\u2013352 (2017)","journal-title":"Neurocomputing"},{"key":"41_CR15","unstructured":"Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142\u2013150 (2011)"},{"key":"41_CR16","unstructured":"McCallum, A., Nigam, K., et al.: A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, pp. 41\u201348. Citeseer (1998)"},{"key":"41_CR17","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"41_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1007\/11744085_38","volume-title":"Computer Vision \u2013 ECCV 2006","author":"E Nowak","year":"2006","unstructured":"Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490\u2013503. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744085_38"},{"key":"41_CR19","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"41_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-642-15561-1_11","volume-title":"Computer Vision \u2013 ECCV 2010","author":"F Perronnin","year":"2010","unstructured":"Perronnin, F., S\u00e1nchez, J., Mensink, T.: Improving the Fisher Kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143\u2013156. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_11"},{"key":"41_CR22","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)"},{"key":"41_CR23","doi-asserted-by":"crossref","unstructured":"Picard, D., Gosselin, P.H.: Improving image similarity with vectors of locally aggregated tensors. In: International Conference on Image Processing, pp. 669\u2013672. IEEE (2011)","DOI":"10.1109\/ICIP.2011.6116641"},{"key":"41_CR24","unstructured":"Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133\u2013142 (2003)"},{"issue":"4","key":"41_CR25","first-page":"1","volume":"5","author":"D Ramyachitra","year":"2014","unstructured":"Ramyachitra, D., Manikandan, P.: Imbalanced dataset classification and solutions: a review. Int. J. Comput. Bus. Res. 5(4), 1\u201329 (2014)","journal-title":"Int. J. Comput. Bus. Res."},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Wallach, H.M.: Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 977\u2013984. ACM (2006)","DOI":"10.1145\/1143844.1143967"},{"key":"41_CR27","doi-asserted-by":"crossref","unstructured":"Yang, J., Jiang, Y.G., Hauptmann, A.G., Ngo, C.W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, pp. 197\u2013206. ACM (2007)","DOI":"10.1145\/1290082.1290111"},{"issue":"1\u20134","key":"41_CR28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s13042-010-0001-0","volume":"1","author":"Y Zhang","year":"2010","unstructured":"Zhang, Y., Jin, R., Zhou, Z.H.: Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. and Cybern. 1(1\u20134), 43\u201352 (2010)","journal-title":"Int. J. Mach. Learn. and Cybern."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46147-8_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T09:22:34Z","timestamp":1746523354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46147-8_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461461","9783030461478"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46147-8_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"18% - 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":"3.04","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":"5.3","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)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}