{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:06:48Z","timestamp":1743080808819,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319703527"},{"type":"electronic","value":"9783319703534"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","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":[[2017]]},"DOI":"10.1007\/978-3-319-70353-4_11","type":"book-chapter","created":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T23:37:40Z","timestamp":1511307460000},"page":"126-137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sensing Forest for Pattern Recognition"],"prefix":"10.1007","author":[{"given":"Irina","family":"Burciu","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Martinetz","sequence":"additional","affiliation":[]},{"given":"Erhardt","family":"Barth","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,11,23]]},"reference":[{"issue":"3","key":"11_CR1","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1037\/h0084327","volume":"46","author":"JK O\u2019Regan","year":"1992","unstructured":"O\u2019Regan, J.K.: Solving the \u201creal\u201d mysteries of visual perception: the world as an outside memory. Can. J. Psychol. 46(3), 461\u2013488 (1992)","journal-title":"Can. J. Psychol."},{"issue":"2","key":"11_CR2","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/TBCAS.2015.2414881","volume":"9","author":"SC Liu","year":"2015","unstructured":"Liu, S.C., Yang, M.H., Steiner, A., Moeckel, R., Delbruck, T.: 1\u00a0kHz 2D visual motion sensor using 20 x 20 silicon retina optical sensor and DSP microcontroller. IEEE Trans. Biomed. Circ. Syst. 9(2), 207\u2013216 (2015)","journal-title":"IEEE Trans. Biomed. Circ. Syst."},{"issue":"2","key":"11_CR3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2007.914731","volume":"25","author":"E Cand\u00e8s","year":"2008","unstructured":"Cand\u00e8s, E., Wakin, M.: Introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21\u201330 (2008)","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"11_CR4","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1088\/0954-898X_7_2_014","volume":"7","author":"B Olshausen","year":"1996","unstructured":"Olshausen, B., Field, D.: Natural image statistics and efficient coding. Netw. Comput. Neural Syst. 7(2), 333\u2013339 (1996)","journal-title":"Netw. Comput. Neural Syst."},{"issue":"4","key":"11_CR5","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"D Donoho","year":"2006","unstructured":"Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289\u20131306 (2006)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Burciu, I., Ion-M\u0103rgineanu, A., Martinetz, T., Barth, E.: Visual manifold sensing. In: Rogowitz, B.E., Pappas, T.N., de Ridder, H. (eds.) Proceedings of SPIE Electronic Imaging Human Vision and Electronic Imaging XIX, vol. 9014, pp. 48:1\u201348:8 (2014)","DOI":"10.1117\/12.2043012"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Burciu, I., Martinetz, T., Barth, E.: Foveated manifold sensing for object recognition. In: Proceedings of IEEE Black Sea Conference on Communications and Networking, pp. 196\u2013200 (2015)","DOI":"10.1109\/BlackSeaCom.2015.7185114"},{"key":"11_CR8","doi-asserted-by":"crossref","first-page":"2268","DOI":"10.1126\/science.290.5500.2268","volume":"290","author":"H Seung","year":"2000","unstructured":"Seung, H., Lee, D.: The manifold ways of perception. Science 290, 2268\u20132269 (2000)","journal-title":"Science"},{"issue":"8","key":"11_CR9","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.tics.2007.06.010","volume":"11","author":"J DiCarlo","year":"2007","unstructured":"DiCarlo, J., Cox, D.: Untangling invariant object recognition. Trends Cogn. Sci. 11(8), 333\u2013341 (2007)","journal-title":"Trends Cogn. Sci."},{"issue":"2","key":"11_CR10","doi-asserted-by":"crossref","first-page":"20402:1","DOI":"10.2352\/J.ImagingSci.Technol.2016.60.2.020402","volume":"60","author":"I Burciu","year":"2016","unstructured":"Burciu, I., Martinetz, T., Barth, E.: Hierarchical manifold sensing with foveation and adaptive partitioning of the dataset. J. Imaging Sci. Technol. 60(2), 20402:1\u201320402:10 (2016)","journal-title":"J. Imaging Sci. Technol."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Sch\u00fctze, H., Barth, E., Martinetz, T.: An adaptive hierarchical sensing scheme for sparse signals. In: Rogowitz, B.E., Pappas, T.N., de Ridder, H. (eds.) Proceedings of SPIE Electronic Imaging, Human Vision and Electronic Imaging XIX, vol. 9014, pp. 15:1\u201315:8 (2014)","DOI":"10.1117\/12.2043082"},{"key":"11_CR12","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2001","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York (2001). https:\/\/doi.org\/10.1007\/978-0-387-21606-5"},{"key":"11_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-642-35289-8_30","volume-title":"Neural Networks: Tricks of the Trade","author":"A Coates","year":"2012","unstructured":"Coates, A., Ng, A.Y.: Learning feature representations with k-means. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 561\u2013580. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_30"},{"key":"11_CR14","unstructured":"Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: SODA 2007 Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027\u20131035 (2007)"},{"issue":"1","key":"11_CR15","first-page":"65","volume":"7","author":"T Villmann","year":"2017","unstructured":"Villmann, T., Bohnsack, A., Kaden, M.: Can learning vector quantization be an alternative to SVM and deep learning? Recent trends and advanced variants of learning vector quantization for classification learning. JAISCR 7(1), 65\u201381 (2017)","journal-title":"JAISCR"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Kohonen, T.: Improved versions of learning vector quantization. In: IJCNN International Joint Conference on Neural Networks (1990)","DOI":"10.1109\/IJCNN.1990.137622"},{"issue":"3","key":"11_CR17","first-page":"511524","volume":"25","author":"D Nova","year":"2014","unstructured":"Nova, D., Estvez, P.A.: A review of learning vector quantization classifiers. Neural Comput. Appl. 25(3), 511524 (2014)","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"11_CR18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forest. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"11_CR19","unstructured":"Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-100). Technical report CUCS-006-96 (1996)"},{"issue":"11","key":"11_CR20","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Advanced Concepts for Intelligent Vision Systems"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-70353-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,6]],"date-time":"2019-10-06T11:22:46Z","timestamp":1570360966000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-70353-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319703527","9783319703534"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-70353-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}