Double similarities weighted multi-instance learning kernel and its application
Authors: 
Jianan Zhang, Yongfei Wu, Fang Hao, Xueyu Liu, Ming Li, Daoxiang Zhou, Wen ZhengAuthors Info & Claims
References
[1]
Andrews, S., Tsochantaridis, I., & Hofmann, T. (2002). Support Vector Machines for Multiple-Instance Learning. In Proceedings of conference and workshop on neural information processing systems.
[2]
Avolio M., Fuduli A., A semiproximal support vector machine approach for binary multiple instance learning, IEEE Transactions on Neural Networks and Learning Systems (2020),.
[3]
Bero S.A., Muda A.K., Choo Y.-H., Muda N.A., Pratama S.F., Weighted tanimoto coefficient for 3D molecule structure similarity measurement, 2018,. arXiv preprint arXiv:1806.05237.
[4]
Blaschko, M. B., & Hofmann, T. (2006). Conformal multi-instance kernels. In Proceedings of Conference and Workshop on Neural Information Processing Systems.
[5]
Carbonneau M.-A., Cheplygina V., Granger E., Gagnon G., Multiple instance learning: A survey of problem characteristics and applications, Pattern Recognition 77 (2018) 329–353,.
[6]
Carbonneau M.-A., Granger E., Raymond A.J., Gagnon G., Robust multiple-instance learning ensembles using random subspace instance selection, Pattern Recognition 58 (2016) 83–99,.
[7]
Chen Y., Bi J., Wang J.Z., MILES: Multiple-instance learning via embedded instance selection, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (12) (2006) 1931–1947,.
[8]
Chen Y., Wang J.Z., Image categorization by learning and reasoning with regions, Journal of Machine Learning Research 5 (2004) 913–939.
[9]
Cheplygina V., Tax D.M., Loog M., Multiple instance learning with bag dissimilarities, Pattern Recognition 48 (1) (2015) 264–275,.
[10]
Demšar J., Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research 7 (2006) 1–30.
[11]
Dietterich T.G., Lathrop R.H., Lozano-Pérez T., Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence 89 (1–2) (1997) 31–71,.
[12]
Doran G., Ray S., A theoretical and empirical analysis of support vector machine methods for multiple-instance classification, Machine Learning 97 (1) (2014) 79–102,.
[13]
Dubuisson M.-P., Jain A.K., A modified hausdorff distance for object matching, in: Proceedings of international conference on pattern recognition, 1994,.
[14]
Foulds J., Frank E., A review of multi-instance learning assumptions, The Knowledge Engineering Review 25 (1) (2010) 1–25,.
[15]
Frey B.J., Dueck D., Clustering by passing messages between data points, Science 315 (5814) (2007) 972–976,.
[16]
Friedman M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Publications of the American Statistical Association 32 (200) (1939) 675–701,.
[17]
Gärtner, T., Flach, P. A., Kowalczyk, A., & Smola, A. J. (2002). Multi-instance kernels. In Proceedings of international conference on machine learning.
[18]
He C., Shao J., Zhang J., Zhou X., Clustering-based multiple instance learning with multi-view feature, Expert Systems with Applications 162 (2020),.
[19]
Hu T., Zhang L., Xie L., Yi Z., A multi-instance networks with multiple views for classification of mammograms, Neurocomputing 443 (2021) 320–328,.
[20]
Huang S., Liu Z., Jin W., Mu Y., Bag dissimilarity regularized multi-instance learning, Pattern Recognition 126 (2022),.
[21]
Ilse M., Tomczak J., Welling M., Attention-based deep multiple instance learning, in: International conference on machine learning, PMLR, 2018, pp. 2127–2136.
[22]
Kwok, J. T., & Cheung, P.-M. (2007). Marginalized multi-instance kernels. In Proceedings of international joint conference on artificial intelligence.
[23]
Li W.-J., yeung D.Y., MILD: Multiple-instance learning via disambiguation, IEEE Transactions on Knowledge and Data Engineering 22 (1) (2009) 76–89,.
[24]
Melki G., Cano A., Ventura S., MIRSVM: multi-instance support vector machine with bag representatives, Pattern Recognition 79 (2018) 228–241,.
[25]
Papastergiou T., Zacharaki E.I., Megalooikonomou V., TensMIL2: Improved multiple instance classification through tensor decomposition and instance selection, in: 2019 27th European signal processing conference, 2019, pp. 1–5,.
[26]
Settles B., Craven M., Ray S., Multiple-instance active learning, Advances in Neural Information Processing Systems 20 (2007) 1289–1296.
[27]
Sudharshan P., Petitjean C., Spanhol F., Oliveira L.E., Heutte L., Honeine P., Multiple instance learning for histopathological breast cancer image classification, Expert Systems with Applications 117 (2019) 103–111,.
[28]
Wang X., Wei D., Cheng H., Fang J., Multi-instance learning based on representative instance and feature mapping, Neurocomputing 216 (2016) 790–796,.
[29]
Wei X.-S., Wu J., Zhou Z.-H., Scalable algorithms for multi-instance learning, IEEE Transactions on Neural Networks and Learning Systems 28 (4) (2016) 975–987,.
[30]
Wen C., Li Z., Qu J., Fan Q., Li A., MILDMS: Multiple instance learning via DD constraint and multiple part similarity, Symmetry 11 (9) (2019) 1080,.
[31]
Xiao Y., Liu B., Hao Z., Cao L., A similarity-based classification framework for multiple-instance learning, IEEE Transactions on Cybernetics 44 (4) (2013) 500–515,.
[32]
Xu X., Statistical learning in multiple instance problems, (Ph.D. thesis) The University of Waikato, 2003.
[33]
Xu X., Frank E., Logistic regression and boosting for labeled bags of instances, in: Proceedings of Pacific-Asia conference on knowledge discovery and data mining, 2004,.
[34]
Xu B.-C., Ting K.M., Zhou Z.-H., Isolation set-kernel and its application to multi-instance learning, in: Proceedings of conference on knowledge discovery and data mining, 2019,.
[35]
Zhang C., Platt J., Viola P., Multiple instance boosting for object detection, Advances in Neural Information Processing Systems 18 (2005) 1417–1424.
[36]
Zhang Y., Surendran A.C., Platt J.C., Narasimhan M., Learning from multi-topic web documents for contextual advertisement, in: Proceedings of conference on knowledge discovery and data mining, 2008,.
[37]
Zhou Z.-H., Sun Y.-Y., Li Y.-F., Multi-instance learning by treating instances as non-iid samples, in: Proceedings of international conference on machine learning, 2009,.
[38]
Zhou Z.-H., Zhang M.-L., Solving multi-instance problems with classifier ensemble based on constructive clustering, Knowledge and Information Systems 11 (2) (2007) 155–170,.
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Published: 27 February 2024
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