{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:20:20Z","timestamp":1743124820859,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319112053"},{"type":"electronic","value":"9783319112060"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-11206-0_8","type":"book-chapter","created":{"date-parts":[[2014,9,15]],"date-time":"2014-09-15T01:31:55Z","timestamp":1410744715000},"page":"68-79","source":"Crossref","is-referenced-by-count":1,"title":["Environment-Adaptive Learning: How Clustering Helps to Obtain Good Training Data"],"prefix":"10.1007","author":[{"given":"Shoubhik","family":"Debnath","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiv Sankar","family":"Baishya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rudolph","family":"Triebel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varun","family":"Dutt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Cremers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"8_CR1","unstructured":"Zhu, X.: Semi-supervised learning literature survey. Computer Sciences, University of Wisconsin-Madison, Tech. Rep. 1530 (2005)"},{"key":"8_CR2","unstructured":"Zhu, X.: Semi-supervised learning with graphs. Ph.D. dissertation, Carnegie Mellon University (2005)"},{"key":"8_CR3","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/11559887_4","volume-title":"Deterministic and Statistical Methods in Machine Learning","author":"N.D. Lawrence","year":"2005","unstructured":"Lawrence, N.D., Platt, J.C., Jordan, M.I.: Extensions of the informative vector machine. In: Winkler, J.R., Niranjan, M., Lawrence, N.D. (eds.) Machine Learning Workshop. LNCS (LNAI), vol.\u00a03635, pp. 56\u201387. Springer, Heidelberg (2005)"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Saffari, A., Leistner, C., Bischof, H.: Regularized multi-class semi-supervised boosting. In: Conf. on Comp. Vision & Patt. Recog., CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206715"},{"key":"8_CR5","unstructured":"Joachims, T.: Transductive inference for text classification using support vector machines, pp. 200\u2013209 (1999)"},{"key":"8_CR6","unstructured":"Triebel, R., Paul, R., Rus, D., Newman, P.: Parsing outdoor scenes from streamed 3D laser data using online clustering and incremental belief updates. In: Robotics Track of AAAI Conference on Artificial Intelligence (2012)"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi-supervised learning for image classification. In: Conf. on Comp. Vision & Patt. Recog., CVPR (2010)","DOI":"10.1109\/CVPR.2010.5540120"},{"key":"8_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1007\/978-3-642-15549-9_52","volume-title":"Computer Vision \u2013 ECCV 2010","author":"S. Ebert","year":"2010","unstructured":"Ebert, S., Larlus, D., Schiele, B.: Extracting structures in image collections for object recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol.\u00a06311, pp. 720\u2013733. Springer, Heidelberg (2010)"},{"issue":"11","key":"8_CR9","doi-asserted-by":"publisher","first-page":"2751","DOI":"10.1109\/TPAMI.2013.54","volume":"35","author":"I. Budvytis","year":"2013","unstructured":"Budvytis, I., Badrinarayanan, V., Cipolla, R.: Semi-supervised video segmentation using tree structured graphical models. Trans. on Pattern Analysis and Machine Intelligence\u00a035(11), 2751\u20132764 (2013)","journal-title":"Trans. on Pattern Analysis and Machine Intelligence"},{"key":"8_CR10","unstructured":"Settles, B.: Active learning literature survey. Tech. Rep. (2010)"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11263-009-0268-3","volume":"88","author":"A. Kapoor","year":"2010","unstructured":"Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Gaussian processes for object categorization. Intern. Journal of Computer Vision\u00a088(2), 169\u2013188 (2010)","journal-title":"Intern. Journal of Computer Vision"},{"key":"8_CR12","unstructured":"Triebel, R., Grimmett, H., Paul, R., Posner, I.: Driven learning for driving: How introspection improves semantic mapping. In: The International Symposium on Robotics Research, ISRR (2013)"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. of the Intern. Conf. on Computer Vision (ICCV), pp. 1150\u20131157 (1999)","DOI":"10.1109\/ICCV.1999.790410"},{"issue":"11","key":"8_CR14","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R. Achanta","year":"2012","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. Trans. on Pattern Analysis and Machine Intelligence\u00a034(11), 2274\u20132282 (2012)","journal-title":"Trans. on Pattern Analysis and Machine Intelligence"},{"key":"8_CR15","unstructured":"Rosenberg, A., Hirschberg, J.: V-measure: A conditional entropy-based external cluster evaluation measure. In: Proc. of the Joint Conf. on Empirical Methods in Natural Language Proc. and Comp. Natural Language Learning (EMNLP-CoNLL), pp. 410\u2013420 (2007)"},{"issue":"4","key":"8_CR16","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U. Luxburg","year":"2007","unstructured":"Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing\u00a017(4), 395\u2013416 (2007)","journal-title":"Statistics and Computing"},{"key":"8_CR17","unstructured":"Bo, L., Ren, X., Fox, D.: Hierarchical matching pursuit for image classification: Architecture and fast algorithms. In: NIPS (2011)"}],"container-title":["Lecture Notes in Computer Science","KI 2014: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-11206-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T10:14:53Z","timestamp":1674814493000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-11206-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319112053","9783319112060"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-11206-0_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2014]]}}}