{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T17:45:41Z","timestamp":1762623941330,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T00:00:00Z","timestamp":1510617600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T00:00:00Z","timestamp":1510617600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2019,3,1]]},"DOI":"10.1007\/s13042-017-0736-y","type":"journal-article","created":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T07:34:36Z","timestamp":1510644876000},"page":"541-547","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Haptic recognition using hierarchical extreme learning machine with local-receptive-field"],"prefix":"10.1007","volume":"10","author":[{"given":"Fengxue","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4042-6044","authenticated-orcid":false,"given":"Huaping","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xinying","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Fuchun","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,11,14]]},"reference":[{"issue":"5","key":"736_CR1","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1152\/jn.1996.75.5.1963","volume":"75","author":"G Cadoret","year":"1996","unstructured":"Cadoret G, Smith AM (1996) Friction, not texture, dictates grip forces used during object manipulation. J Neurophysiol 75(5):1963\u20131969","journal-title":"J Neurophysiol"},{"issue":"5","key":"736_CR2","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/nrn2621","volume":"10","author":"RS Johansson","year":"2009","unstructured":"Johansson RS, Flanagan JR (2009) Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat Rev Neurosci 10(5):345\u2013359","journal-title":"Nat Rev Neurosci"},{"issue":"6","key":"736_CR3","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1109\/TRO.2011.2162271","volume":"27","author":"JM Romano","year":"2011","unstructured":"Romano JM, Hsiao K, Niemeyer G et al (2011) Human-inspired robotic grasp control with tactile sensing. IEEE Trans Robot 27(6):1067\u20131079","journal-title":"IEEE Trans Robot"},{"key":"736_CR4","doi-asserted-by":"crossref","unstructured":"Gorostiza, JF, Salichs MA (2011) End-user programming of a social robot by dialog. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 1102\u20131114","DOI":"10.1016\/j.robot.2011.07.009"},{"key":"736_CR5","unstructured":"Brooks DJ, Lignos C, Finucane C et al (2012) Make it so: continuous, flexible natural language interaction with an autonomous robot. Grounding language for physical systems workshop at the AAAI conference on artificial intelligence"},{"issue":"3","key":"736_CR6","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1109\/TIP.2016.2516952","volume":"25","author":"L Zhang","year":"2016","unstructured":"Zhang L, Zuo W, Zhang D (2016) LSDT: latent sparse domain transfer learning for visual adaptation. IEEE Trans Image Process 25(3):1177\u20131191","journal-title":"IEEE Trans Image Process"},{"issue":"Jul","key":"736_CR7","first-page":"2211","volume":"12","author":"M G\u00f6nen","year":"2011","unstructured":"G\u00f6nen M, Alpayd\u0131n E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12(Jul):2211\u20132268","journal-title":"J Mach Learn Res"},{"key":"736_CR8","doi-asserted-by":"publisher","unstructured":"Liu H, Wu Y, Sun F, Fang B, Guo D Weakly-paired multi-modal fusion for object recognition. IEEE Trans Autom Sci Eng (in press). https:\/\/doi.org\/10.1109\/TASE.2017.2692271","DOI":"10.1109\/TASE.2017.2692271"},{"issue":"7","key":"736_CR9","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1109\/TSMC.2016.2635141","volume":"47","author":"H Liu","year":"2017","unstructured":"Liu H, Sun F, Guo D, Fang B (2017) Structured output-associated dictionary learning for haptic understanding. IEEE Trans Syst Man Cybern Syst 47(7):1564\u20131574","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"12","key":"736_CR10","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1109\/TCYB.2016.2614809","volume":"47","author":"H Liu","year":"2017","unstructured":"Liu H, Qin J, Sun F et al (2017) Extreme kernel sparse learning for tactile object recognition. IEEE Trans Cybern 47(12):4509\u20134520","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"736_CR11","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/TASE.2016.2549552","volume":"14","author":"H Liu","year":"2017","unstructured":"Liu H, Yu Y, Sun F, Gu J (2017) Visual-tactile fusion for object recognition. IEEE Trans Autom Sci Eng 14(2):996\u20131008","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"3","key":"736_CR12","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1109\/TIM.2016.2514779","volume":"65","author":"H Liu","year":"2016","unstructured":"Liu H, Guo D, Sun F (2016) Object recognition using tactile measurements: kernel sparse coding methods. IEEE Trans Instrum Meas 65(3):656\u2013665","journal-title":"IEEE Trans Instrum Meas"},{"issue":"12","key":"736_CR13","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.1109\/TMM.2016.2598140","volume":"18","author":"H Zheng","year":"2016","unstructured":"Zheng H, Fang L, Ji M et al. (2016) Deep learning for surface material classification using haptic and visual information. IEEE Trans Multimed 18(12):2407\u20132416","journal-title":"IEEE Trans Multimed"},{"key":"736_CR14","doi-asserted-by":"crossref","unstructured":"Gao Y, Hendricks LA, Kuchenbecker KJ et al (2016) Deep learning for tactile understanding from visual and haptic data. Robotics and automation (ICRA), 2016 IEEE international conference on. IEEE, pp 536\u2013543","DOI":"10.1109\/ICRA.2016.7487176"},{"issue":"1","key":"736_CR15","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"key":"736_CR16","doi-asserted-by":"crossref","unstructured":"Huang G, Zhou H, Ding X, Zhang R, (2012) Extreme learning machine for regression and multi-class classification. In: Proceedings of the IEEE Systems Man and Cybernetics Society, pp 513\u2013529","DOI":"10.1109\/TSMCB.2011.2168604"},{"issue":"10","key":"736_CR17","doi-asserted-by":"publisher","first-page":"4528","DOI":"10.1016\/j.jfranklin.2015.07.002","volume":"352","author":"J Cao","year":"2015","unstructured":"Cao J, Zhao Y, Lai X et al. (2015) Landmark recognition with sparse representation classification and extreme learning machine. J Frankl Inst 352(10):4528\u20134545","journal-title":"J Frankl Inst"},{"issue":"6","key":"736_CR18","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MIS.2013.140","volume":"28","author":"E Cambria","year":"2013","unstructured":"Cambria E, Huang GB, Kasun LLC et al. (2013) Extreme learning machines [trends & controversies]. IEEE Intell Syst 28(6):30\u201359","journal-title":"IEEE Intell Syst"},{"issue":"11","key":"736_CR19","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/TCYB.2015.2481713","volume":"46","author":"Y Yang","year":"2016","unstructured":"Yang Y, Wu QMJ (2016) Multilayer extreme learning machine with subnetwork nodes for representation learning. IEEE Trans Cybern 46(11):2570\u20132583","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"736_CR20","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"J Tang","year":"2016","unstructured":"Tang J, Deng C, Huang GB (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809\u2013821","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"736_CR21","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhang D (2016) Evolutionary cost-sensitive extreme learning machine. IEEE Trans Neural Netw","DOI":"10.1109\/TNNLS.2016.2607757"},{"issue":"10","key":"736_CR22","doi-asserted-by":"publisher","first-page":"4959","DOI":"10.1109\/TIP.2016.2598679","volume":"25","author":"L Zhang","year":"2016","unstructured":"Zhang L, Zhang D (2016) Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Trans Image Process 25(10):4959\u20134973","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"736_CR23","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1109\/TIM.2014.2367775","volume":"64","author":"L Zhang","year":"2015","unstructured":"Zhang L, Zhang D (2015) Domain adaptation extreme learning machines for drift compensation in E-nose systems. IEEE Instrum Meas 64(7):1790\u20131801","journal-title":"IEEE Instrum Meas"},{"key":"736_CR24","doi-asserted-by":"crossref","unstructured":"Huang G, Bai Z, Kasun LLC, Chi MV (2015) Local receptive fields based extreme learning machine. In: Proceedings of the IEEE computational intelligence magazine, pp 18\u201329","DOI":"10.1109\/MCI.2015.2405316"},{"key":"736_CR25","doi-asserted-by":"crossref","unstructured":"Schwarz M, Schulz H, Behnke S (2015) RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. In: Proceedings of the IEEE internatioanl conference on robotics and automation (ICRA), pp 1329\u20131335","DOI":"10.1109\/ICRA.2015.7139363"},{"key":"736_CR26","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"736_CR27","doi-asserted-by":"crossref","unstructured":"Huang W, Hong H, Song, G, Xie K (2014) Deep process neural network for temporal deep learning. In: Proceedings of the international joint conference neural networks (IJCNN), pp 465\u2013472","DOI":"10.1109\/IJCNN.2014.6889533"},{"issue":"7553","key":"736_CR28","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"2","key":"736_CR29","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/s13042-015-0351-8","volume":"8","author":"S Ding","year":"2017","unstructured":"Ding S, Zhang N, Zhang J (2017) Unsupervised extreme learning machine with representational features. Int J Mach Learn Cybern 8(2):587\u2013595","journal-title":"Int J Mach Learn Cybern"},{"key":"736_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-017-0666-8","author":"L Hu","year":"2017","unstructured":"Hu L, Chen Y, Wang J et al. (2017) OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition. Int J Mach Learn Cybern 1\u20137. https:\/\/doi.org\/10.1007\/s13042-017-0666-8","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"736_CR31","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s13042-015-0419-5","volume":"7","author":"J Zhang","year":"2016","unstructured":"Zhang J, Ding S, Zhang N et al (2016) Incremental extreme learning machine based on deep feature embedded. Int J Mach Learn Cybern 7(1):111\u2013120","journal-title":"Int J Mach Learn Cybern"},{"issue":"5","key":"736_CR32","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/s13042-014-0292-7","volume":"7","author":"P Liu","year":"2016","unstructured":"Liu P, Huang Y, Meng L (2016) Two-stage extreme learning machine for high-dimensional data. Int J Mach Learn Cybern 7(5):765\u2013772","journal-title":"Int J Mach Learn Cybern"},{"issue":"3","key":"736_CR33","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s13042-014-0310-9","volume":"7","author":"H Zhao","year":"2016","unstructured":"Zhao H, Li G, Zhang H (2016) An improved algorithm for segmenting online time series with error bound guarantee. Int J Mach Learn Cybern 7(3):365\u2013374","journal-title":"Int J Mach Learn Cybern"},{"key":"736_CR34","unstructured":"Chu V, McMahon L, Riano L, McDonald CG, He Q, Perez-Tejada JM, Arrigo M, Darrell T, Kuchenbecker KJ, (2015) Robotic learing of haptic adjectives through physical interaction. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 279\u2013292"},{"key":"736_CR35","unstructured":"Chu V, McMahon L, Riano L, McDonald CG, He Q, Perez-Tejada JM, Arrigo M, Fitter N, Nappo J, Darrell T, Kuchenbecker KJ (2013) Using robotic explorarory procedures to learn the meaning of haptic adjectives. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 3048\u20133055"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-017-0736-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0736-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0736-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T21:59:19Z","timestamp":1613771959000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-017-0736-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,14]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,3,1]]}},"alternative-id":["736"],"URL":"https:\/\/doi.org\/10.1007\/s13042-017-0736-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2017,11,14]]},"assertion":[{"value":"28 September 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}