Grey-based multiple instance learning with multiple bag-representative

[1]  Razvan C. Bunescu,et al.  Multiple instance learning for sparse positive bags , 2007, ICML '07.

[2]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[3]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[4]  Marco Loog,et al.  Multiple instance learning with bag dissimilarities , 2013, Pattern Recognit..

[5]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[6]  Jing Hua,et al.  Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[8]  Chi-Chun Huang,et al.  A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction , 2004, Applied Intelligence.

[9]  JIANPING LI,et al.  Feature Selection via Least Squares Support Feature Machine , 2007, Int. J. Inf. Technol. Decis. Mak..

[10]  Jianping Li,et al.  A weighted Lq adaptive least squares support vector machine classifiers - Robust and sparse approximation , 2011, Expert Syst. Appl..

[11]  Vojislav Kecman,et al.  Iterative k Data Algorithm for solving both the least squares SVM and the system of linear equations , 2015, SoutheastCon 2015.

[12]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

[13]  Sebastián Ventura,et al.  Discovering useful patterns from multiple instance data , 2016, Inf. Sci..

[14]  Yixin Chen,et al.  MILES: Multiple-Instance Learning via Embedded Instance Selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Adam Tauman Kalai,et al.  A Note on Learning from Multiple-Instance Examples , 2004, Machine Learning.

[16]  Mark Craven,et al.  Supervised versus multiple instance learning: an empirical comparison , 2005, ICML.

[17]  Sebastián Ventura,et al.  Speeding up multiple instance learning classification rules on GPUs , 2015, Knowledge and Information Systems.

[18]  Vojislav Kecman,et al.  Algorithms for direct L2 support vector machines , 2014, 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings.

[19]  Jun Wang,et al.  Solving the Multiple-Instance Problem: A Lazy Learning Approach , 2000, ICML.

[20]  Peter Auer,et al.  On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach , 1997, ICML.

[21]  Jun Zhou,et al.  MILIS: Multiple Instance Learning with Instance Selection , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Vojislav Kecman,et al.  Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning , 2006, Studies in Computational Intelligence.

[23]  Jianping Li,et al.  Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis , 2011, Expert Syst. Appl..

[24]  Sebastián Ventura,et al.  MIRSVM: Multi-instance support vector machine with bag representatives , 2018, Pattern Recognit..

[25]  Qi Zhang,et al.  Content-Based Image Retrieval Using Multiple-Instance Learning , 2002, ICML.

[26]  Zhi-Hua Zhou,et al.  Multi-instance learning by treating instances as non-I.I.D. samples , 2008, ICML '09.

[27]  Chengqi Zhang,et al.  Multi-Instance Learning from Positive and Unlabeled Bags , 2014, PAKDD.