{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:24:04Z","timestamp":1750220644427,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,11,6]]},"DOI":"10.1145\/3443467.3443763","type":"proceedings-article","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T23:37:36Z","timestamp":1612222656000},"page":"253-257","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparseness of the LS-SVM algorithm based on the leave one out cross validation method with Multibeam data"],"prefix":"10.1145","author":[{"given":"Bo","family":"Zhang","sequence":"first","affiliation":[{"name":"Naval Research Academy, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Naval Research Academy, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Fan","sequence":"additional","affiliation":[{"name":"Naval Research Academy, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guobin","family":"Chang","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,2]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"62","article-title":"A new algorithm for automatic processing of bathymetric data. Oceanic Engineering","volume":"28","author":"Canepa Bergem","year":"2003","unstructured":"Canepa, Bergem, and Pace. (2003). A new algorithm for automatic processing of bathymetric data. Oceanic Engineering, IEEE Journal of. 28. 62 - 77. 10.1109\/JOE.2002.808204.","journal-title":"IEEE Journal of."},{"key":"e_1_3_2_1_2_1","first-page":"1","article-title":"Oceans - Yeosu","volume":"2012","author":"Xu H. Shi","year":"2012","unstructured":"W. Xu, H. Shi and H. Zhang, Sparse-reconstruction-based high resolution beamforming and its application to multi-beam systems. 2012 Oceans - Yeosu, Yeosu, 2012: pp. 1--4.","journal-title":"Yeosu"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Ferreira Santos and et al. (2019). Robust methodology for detection of spikes in multibeam echo sounder data. Boletim de Ci\u00eancias Geod\u00e9sicas. 25. 10.1590\/s1982-21702019000300014.","DOI":"10.1590\/s1982-21702019000300014"},{"key":"e_1_3_2_1_4_1","article-title":"Integration of Hyperspectral Imagery and Sparse Sonar Data for Shallow Water Bathymetry Mapping. Geoscience and Remote Sensing","author":"Cheng L.","year":"2015","unstructured":"Cheng, L. et al. 2015. Integration of Hyperspectral Imagery and Sparse Sonar Data for Shallow Water Bathymetry Mapping. Geoscience and Remote Sensing. IEEE Transactions on, 3235--3249.","journal-title":"IEEE Transactions on, 3235--3249."},{"key":"e_1_3_2_1_5_1","first-page":"35","volume-title":"The influence of the grid resolution on the accuracy of the digital terrain model used in seabed modeling. Marine Geophysical Research, v. 36, n. 1","author":"Maleika W.","unstructured":"Maleika, W. 2015. The influence of the grid resolution on the accuracy of the digital terrain model used in seabed modeling. Marine Geophysical Research, v. 36, n. 1, p. 35--44."},{"key":"e_1_3_2_1_6_1","volume-title":"Least Squares Support Vector Machine Classifiers. Neural Processing Letters. 9. 293--300. 10.1023\/A:1018628609742","author":"Suykens Johan","year":"1999","unstructured":"Suykens, Johan and Vandewalle, Joos. (1999). Least Squares Support Vector Machine Classifiers. Neural Processing Letters. 9. 293--300. 10.1023\/A:1018628609742."},{"key":"e_1_3_2_1_7_1","first-page":"1109","article-title":"Recurrent Least Squares Support Vector Machines. Circuits and Systems I: Fundamental Theory and Applications","volume":"47","author":"Suykens Johan","year":"2000","unstructured":"Suykens, Johan and Vandewalle, Joos. (2000). Recurrent Least Squares Support Vector Machines. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on. 47. 1109--1114. 10.1109\/81.855471.","journal-title":"IEEE Transactions on."},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05. 1. 279 - 283","author":"Wang Haifeng","year":"2005","unstructured":"Wang, Haifeng and Hu, Dejin. (2005). Comparison of SVM and LS-SVM for regression. Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05. 1. 279 - 283. 10.1109\/ICNNB.2005.1614615."},{"key":"e_1_3_2_1_9_1","volume-title":"Sparse Approximation Using Least Squares Support Vector Machines. 2. 757 - 760","author":"Suykens Johan","year":"2000","unstructured":"Suykens, Johan, Lukas, Lukas and Vandewalle, Joos. (2000). Sparse Approximation Using Least Squares Support Vector Machines. 2. 757 - 760 vol.2. 10.1109\/ISCAS.2000.856439."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Suykens Johan De Brabanter Jos and et al.. (2002). Weighted Least Squares Support Vector Machines: robustness and sparse approximation. Neurocomputing. 48. 85--105. 10.1016\/S0925-2312(01)00644-0.","DOI":"10.1016\/S0925-2312(01)00644-0"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13131-010-0082-3"},{"key":"e_1_3_2_1_12_1","volume-title":"The Influence of Optimized Train Samples on Elimination of Sounding Outliers in the LS-SVM Arithmetic","author":"Huang X.","year":"2011","unstructured":"Huang, X., et al., The Influence of Optimized Train Samples on Elimination of Sounding Outliers in the LS-SVM Arithmetic. 2011. 40(1): p. 22--27."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Molinaro Annette Simon Richard and et al.. (2005). Prediction error estimation: A comparison of resampling methods. Bioinformatics (Oxford England). 21. 3301--7. 10.1093\/bioinformatics\/bti499.","DOI":"10.1093\/bioinformatics\/bti499"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-7-91"},{"key":"e_1_3_2_1_15_1","volume-title":"Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models","author":"Vehtari A.","year":"2014","unstructured":"Vehtari, A., et al., Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. 2014. 17(1): p. 3581--3618."},{"key":"e_1_3_2_1_16_1","volume-title":"Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC","author":"Vehtari A.","year":"2015","unstructured":"Vehtari, A., et al., Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. 2015. 27: p. 1--20."},{"key":"e_1_3_2_1_17_1","first-page":"696","article-title":"Pruning error minimization in least squares support vector machines. Neural Networks","volume":"6","author":"De Kruif Bas","year":"2003","unstructured":"De Kruif, Bas & De Vries, Theo. (2003). Pruning error minimization in least squares support vector machines. Neural Networks, IEEE Transactions on. 6. 696 - 702. 10.1109\/TNN.2003.810597.","journal-title":"IEEE Transactions on."}],"event":{"name":"EITCE 2020: 2020 4th International Conference on Electronic Information Technology and Computer Engineering","acronym":"EITCE 2020","location":"Xiamen China"},"container-title":["Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3443467.3443763","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3443467.3443763","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:13Z","timestamp":1750197733000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3443467.3443763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,6]]},"references-count":17,"alternative-id":["10.1145\/3443467.3443763","10.1145\/3443467"],"URL":"https:\/\/doi.org\/10.1145\/3443467.3443763","relation":{},"subject":[],"published":{"date-parts":[[2020,11,6]]},"assertion":[{"value":"2021-02-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}