Chen, Y.X.[Yi-Xin],
Bi, J.B.[Jin-Bo],
Wang, J.Z.[James Z.],
MILES: Multiple-Instance Learning via Embedded Instance Selection,
PAMI(28), No. 12, December 2006, pp. 1931-1947.
IEEE DOI
0611
BibRef
Earlier: A2, A1, A3:
A Sparse Support Vector Machine Approach to Region-Based Image
Categorization,
CVPR05(I: 1121-1128).
IEEE DOI
0507
Training labels on sets of instances not single instances.
BibRef
McGovern, A.[Amy],
Jensen, D.[David],
Optimistic pruning for multiple instance learning,
PRL(29), No. 9, 1 July 2008, pp. 1252-1260.
Elsevier DOI
0711
Multiple instance learning; Optimistic pruning; Chi-squared
BibRef
Tao, Q.P.[Qing-Ping],
Scott, S.D.[Stephen D.],
Vinodchandran, N.V.,
Osugi, T.T.[Thomas Takeo],
Mueller, B.[Brandon],
Kernels for Generalized Multiple-Instance Learning,
PAMI(30), No. 12, December 2008, pp. 2084-2098.
IEEE DOI
0811
BibRef
Fu, Z.Y.[Zhou-Yu],
Robles-Kelly, A.[Antonio],
Zhou, J.[Jun],
MILIS: Multiple Instance Learning with Instance Selection,
PAMI(33), No. 1, January 2011, pp. 958-977.
IEEE DOI
1104
Deals with collections of instances called bags. Each bag has instances
for feature extraction. Large instance space.
BibRef
Fu, Z.Y.[Zhou-Yu],
Robles-Kelly, A.[Antonio],
An instance selection approach to Multiple instance Learning,
CVPR09(911-918).
IEEE DOI
0906
BibRef
Earlier:
Fast multiple instance learning via L1,2 logistic regression,
ICPR08(1-4).
IEEE DOI
0812
BibRef
And:
On Mixtures of Linear SVMs for Nonlinear Classification,
SSPR08(489-499).
Springer DOI
0812
BibRef
Bergeron, C.[Charles],
Moore, G.[Gregory],
Zaretzki, J.[Jed],
Breneman, C.M.[Curt M.],
Bennett, K.P.[Kristin P.],
Fast Bundle Algorithm for Multiple-Instance Learning,
PAMI(34), No. 6, June 2012, pp. 1068-1079.
IEEE DOI
1205
BibRef
Zhang, T.Z.[Tian-Zhu],
Liu, S.[Si],
Xu, C.S.[Chang-Sheng],
Lu, H.Q.[Han-Qing],
M4L:
Maximum margin Multi-instance Multi-cluster Learning for scene modeling,
PR(46), No. 10, October 2013, pp. 2711-2723.
Elsevier DOI
1306
Scene understanding; Maximum margin clustering; Multiple
instance learning (MIL); Gaussian Mixture Model (GMM); Constrained
Concave-Convex Procedure (CCCP)
BibRef
Zhang, B.[Bang],
Wang, Y.[Yang],
Chen, F.[Fang],
Multilabel Image Classification Via High-Order Label Correlation
Driven Active Learning,
IP(23), No. 3, March 2014, pp. 1430-1441.
IEEE DOI
1403
correlation methods
BibRef
Zhang, B.[Bang],
Wang, Y.[Yang],
Wang, W.[Wei],
Batch mode active learning for multi-label image classification with
informative label correlation mining,
WACV12(401-407).
IEEE DOI
1203
BibRef
And:
Multiple-Instance learning from multiple perspectives:
Combining models for Multiple-Instance learning,
WACV12(481-487).
IEEE DOI
1203
BibRef
Herman, G.[Gunawan],
Ye, G.T.[Ge-Tian],
Wang, Y.[Yang],
Xu, J.[Jie],
Zhang, B.[Bang],
Multi-instance learning with relational information of instances,
WACV09(1-7).
IEEE DOI
0912
BibRef
Hong, R.C.[Ri-Chang],
Wang, M.[Meng],
Gao, Y.[Yue],
Tao, D.C.[Da-Cheng],
Li, X.L.[Xue-Long],
Wu, X.D.[Xin-Dong],
Image Annotation by Multiple-Instance Learning With Discriminative
Feature Mapping and Selection,
Cyber(44), No. 5, May 2014, pp. 669-680.
IEEE DOI
1405
correlation methods
BibRef
Li, T.,
Wang, Y.,
Hong, R.C.[Ri-Chang],
Wang, M.[Meng],
Wu, X.D.[Xin-Dong],
pDisVPL: Probabilistic Discriminative Visual Part Learning for Image
Classification,
MultMedMag(25), No. 4, October 2018, pp. 34-45.
IEEE DOI
1901
Visualization, Detectors, Probabilistic logic, Training,
Feature extraction, Image classification, Computational modeling,
image
BibRef
Chai, J.[Jing],
Ding, X.H.[Xing-Hao],
Chen, H.T.[Hong-Tao],
Li, T.Y.[Ting-Yu],
Multiple-instance discriminant analysis,
PR(47), No. 7, 2014, pp. 2517-2531.
Elsevier DOI
1404
Multiple-instance learning
BibRef
Li, Z.[Zhan],
Geng, G.H.[Guo-Hua],
Feng, J.[Jun],
Peng, J.Y.[Jin-Ye],
Wen, C.[Chao],
Liang, J.L.[Jun-Li],
Multiple instance learning based on positive instance selection and
bag structure construction,
PRL(40), No. 1, 2014, pp. 19-26.
Elsevier DOI
1403
Multiple instance learning (MIL)
BibRef
Cheplygina, V.[Veronika],
Tax, D.M.J.[David M.J.],
Loog, M.[Marco],
Multiple instance learning with bag dissimilarities,
PR(48), No. 1, 2015, pp. 264-275.
Elsevier DOI
1410
Multiple instance learning
BibRef
Cheplygina, V.[Veronika],
Tax, D.M.J.[David M.J.],
Loog, M.[Marco],
On classification with bags, groups and sets,
PRL(59), No. 1, 2015, pp. 11-17.
Elsevier DOI
1505
Multiple instance learning
BibRef
Alpaydin, E.[Ethem],
Cheplygina, V.[Veronika],
Loog, M.[Marco],
Tax, D.M.J.[David M.J.],
Single- vs. multiple-instance classification,
PR(48), No. 9, 2015, pp. 2831-2838.
Elsevier DOI
1506
Classification
BibRef
Carbonneau, M.A.[Marc-André],
Granger, E.[Eric],
Raymond, A.J.[Alexandre J.],
Gagnon, G.[Ghyslain],
Robust multiple-instance learning ensembles using random subspace
instance selection,
PR(58), No. 1, 2016, pp. 83-99.
Elsevier DOI
1606
BibRef
And: A1, A2, A4, Only:
Witness identification in multiple instance learning using random
subspaces,
ICPR16(3639-3644)
IEEE DOI
1705
Classification algorithms, Clustering algorithms,
Knowledge discovery, Prototypes, Robustness, Standards,
Support vector machines, Knowledge Discovery,
Multiple Instance Learning, Random Subspace Methods, Witness, Identification
BibRef
Carbonneau, M.A.[Marc-André],
Granger, E.[Eric],
Gagnon, G.[Ghyslain],
Score thresholding for accurate instance classification in multiple
instance learning,
IPTA16(1-6)
IEEE DOI
1703
learning (artificial intelligence)
BibRef
Ding, X.,
Li, B.,
Xiong, W.,
Guo, W.,
Hu, W.,
Wang, B.,
Multi-Instance Multi-Label Learning Combining Hierarchical Context
and its Application to Image Annotation,
MultMed(18), No. 8, August 2016, pp. 1616-1627.
IEEE DOI
1608
Automation
BibRef
Qiao, M.Y.[Mao-Ying],
Liu, L.[Liu],
Yu, J.[Jun],
Xu, C.[Chang],
Tao, D.C.[Da-Cheng],
Diversified dictionaries for multi-instance learning,
PR(64), No. 1, 2017, pp. 407-416.
Elsevier DOI
1701
Multi-instance learning
BibRef
Xiao, Y.S.[Yan-Shan],
Liu, B.[Bo],
Hao, Z.F.[Zhi-Feng],
A Sphere-Description-Based Approach for Multiple-Instance Learning,
PAMI(39), No. 2, February 2017, pp. 242-257.
IEEE DOI
1702
Internet
BibRef
Wang, J.J.Y.[Jim Jing-Yan],
Tsang, I.W.H.[Ivor Wai-Hung],
Cui, X.F.[Xue-Feng],
Lu, Z.W.[Zhi-Wu],
Gao, X.[Xin],
Multi-instance dictionary learning via multivariate performance
measure optimization,
PR(66), No. 1, 2017, pp. 448-459.
Elsevier DOI
1704
Multi-instance learning
BibRef
Tang, P.[Peng],
Wang, X.G.[Xing-Gang],
Feng, B.[Bin],
Liu, W.Y.[Wen-Yu],
Learning Multi-Instance Deep Discriminative Patterns for Image
Classification,
IP(26), No. 7, July 2017, pp. 3385-3396.
IEEE DOI
1706
Feature extraction, Image representation, Neural networks,
Semantics, Stochastic processes, Support vector machines, Training,
Image classification, deep convolutional neural networks,
discriminative patterns, multi-instance learning, stochastic
gradient decent.
BibRef
Tang, P.[Peng],
Wang, X.G.[Xing-Gang],
Bai, X.[Xiang],
Liu, W.Y.[Wen-Yu],
Multiple Instance Detection Network with Online Instance Classifier
Refinement,
CVPR17(3059-3067)
IEEE DOI
1711
Benchmark testing, Detectors, Manifolds, Object detection, Proposals,
Streaming media, Training
BibRef
Wu, J.X.[Jian-Xin],
Bai, X.[Xiang],
Loog, M.[Marco],
Roli, F.[Fabio],
Zhou, Z.H.[Zhi-Hua],
Editorial of the Special Issue on Multi-instance Learning in Pattern
Recognition and Vision,
PR(71), No. 1, 2017, pp. 444-445.
Elsevier DOI
1707
BibRef
Xu, D.K.[Dong-Kuan],
Wu, J.[Jia],
Li, D.[Dewei],
Tian, Y.J.[Ying-Jie],
Zhu, X.Q.[Xing-Quan],
Wu, X.D.[Xin-Dong],
SALE: Self-adaptive LSH encoding for multi-instance learning,
PR(71), No. 1, 2017, pp. 460-482.
Elsevier DOI
1707
Multi-instance learning
BibRef
Verma, M.[Mridula],
Shukla, K.K.,
A new accelerated proximal gradient technique for regularized
multitask learning framework,
PRL(95), No. 1, 2017, pp. 98-103.
Elsevier DOI
1708
Multitask learning
BibRef
Liu, X.[Xu],
Jiao, L.C.[Li-Cheng],
Zhao, J.Q.[Jia-Qi],
Zhao, J.[Jin],
Zhang, D.[Dan],
Liu, F.[Fang],
Yang, S.Y.[Shu-Yuan],
Tang, X.[Xu],
Deep Multiple Instance Learning-Based Spatial-Spectral
Classification for PAN and MS Imagery,
GeoRS(56), No. 1, January 2018, pp. 461-473.
IEEE DOI
1801
Convolution, Feature extraction, Machine learning,
Neural networks, Spatial resolution, Deep learning, feature fusion,
multiple instance learning
BibRef
Carbonneau, M.A.[Marc-André],
Cheplygina, V.[Veronika],
Granger, E.[Eric],
Gagnon, G.[Ghyslain],
Multiple instance learning: A survey of problem characteristics and
applications,
PR(77), 2018, pp. 329-353.
Elsevier DOI
1802
Multiple instance learning, Weakly supervised learning,
Classification, Multi-instance learning,
Drug activity prediction
BibRef
Song, L.Y.[Ling-Yun],
Liu, J.[Jun],
Qian, B.[Buyue],
Sun, M.X.[Ming-Xuan],
Yang, K.[Kuan],
Sun, M.[Meng],
Abbas, S.[Samar],
A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image
Classification,
IP(27), No. 12, December 2018, pp. 6025-6038.
IEEE DOI
1810
Visualization, Task analysis, Correlation, Feature extraction,
Computer science, Sun, CNN,
context information
BibRef
Asif, A.[Amina],
Minhas, F.U.A.[Fayyaz Ul_Amir Afsar],
An embarrassingly simple approach to neural multiple instance
classification,
PRL(128), 2019, pp. 474-479.
Elsevier DOI
1912
Machine Learning, Classification, Multiple Instance Learning, Neural Networks
BibRef
Li, Z.,
Xu, K.,
Xie, J.,
Bi, Q.,
Qin, K.,
Deep Multiple Instance Convolutional Neural Networks for Learning
Robust Scene Representations,
GeoRS(58), No. 5, May 2020, pp. 3685-3702.
IEEE DOI
2005
Convolutional neural network (CNN),
multiple instance learning (MIL), scene classification, scene representation
BibRef
Huang, X.L.[Xiao-Lan],
Xu, K.[Kai],
Huang, C.[Chuming],
Wang, C.R.[Cheng-Rui],
Qin, K.[Kun],
Multiple Instance Learning Convolutional Neural Networks for
Fine-Grained Aircraft Recognition,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Bhattacharjee, K.[Kamanasish],
Pant, M.[Millie],
Zhang, Y.D.[Yu-Dong],
Satapathy, S.C.[Suresh Chandra],
Multiple Instance Learning with Genetic Pooling for medical data
analysis,
PRL(133), 2020, pp. 247-255.
Elsevier DOI
2005
Multiple Instance Learning (MIL), Genetic Algorithm (GA),
Pooling, Neural network
BibRef
Wang, X.Y.[Xin-Yu],
Xu, H.X.[Hai-Xia],
Yuan, L.M.[Li-Ming],
Dai, W.[Wei],
Wen, X.B.[Xian-Bin],
A Remote-Sensing Scene-Image Classification Method Based on Deep
Multiple-Instance Learning with a Residual Dense Attention ConvNet,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Cersovsky, J.[Josef],
Mohammadi, S.[Sadegh],
Kainmueller, D.[Dagmar],
Hoehne, J.[Johannes],
Towards Hierarchical Regional Transformer-based Multiple Instance
Learning,
BioIm23(3954-3962)
IEEE DOI
2401
BibRef
Bontempo, G.[Gianpaolo],
Lumetti, L.[Luca],
Porrello, A.[Angelo],
Bolelli, F.[Federico],
Calderara, S.[Simone],
Ficarra, E.[Elisa],
Buffer-MIL: Robust Multi-instance Learning with a Buffer-based Approach,
CIAP23(II:1-12).
Springer DOI
2312
BibRef
Liu, K.N.[Kang-Ning],
Zhu, W.C.[Wei-Cheng],
Shen, Y.Q.[Yi-Qiu],
Liu, S.[Sheng],
Razavian, N.[Narges],
Geras, K.J.[Krzysztof J.],
Fernandez-Granda, C.[Carlos],
Multiple Instance Learning via Iterative Self-Paced Supervised
Contrastive Learning,
CVPR23(3355-3365)
IEEE DOI
2309
BibRef
Xie, J.[Jie],
Towsey, M.[Michael],
Zhang, L.[Liang],
Yasumiba, K.[Kiyomi],
Schwarzkopf, L.[Lin],
Zhang, J.L.[Jing-Lan],
Roe, P.[Paul],
Multiple-Instance Multiple-Label Learning for the Classification of
Frog Calls with Acoustic Event Detection,
ICISP16(222-230).
WWW Link.
1606
BibRef
Thandiackal, K.[Kevin],
Chen, B.Q.[Bo-Qi],
Pati, P.[Pushpak],
Jaume, G.[Guillaume],
Williamson, D.F.K.[Drew F. K.],
Gabrani, M.[Maria],
Goksel, O.[Orcun],
Differentiable Zooming for Multiple Instance Learning on Whole-Slide
Images,
ECCV22(XXI:699-715).
Springer DOI
2211
BibRef
Xu, K.X.[Kai-Xin],
Liu, L.Y.[Li-Yang],
Zhao, Z.Y.[Zi-Yuan],
Zeng, Z.[Zeng],
Veeravalli, B.[Bharadwaj],
Object-Aware Self-Supervised Multi-Label Learning,
ICIP22(361-365)
IEEE DOI
2211
Training, Representation learning, Deep learning,
Image segmentation, Image representation, Data models,
Multi-instance Learning
BibRef
Hou, C.Q.[Cun-Qiao],
Sun, Q.[Qiule],
Wang, W.[Wei],
Zhang, J.X.[Jian-Xin],
Shuffle Attention Multiple Instances Learning for Breast Cancer Whole
Slide Image Classification,
ICIP22(466-470)
IEEE DOI
2211
Pathology, Correlation, Codes, Feature extraction, Breast cancer,
Task analysis, Image classification, WSI classification, LSTM
BibRef
Tschuchnig, M.E.[Maximilian E.],
Grubmüller, P.[Philipp],
Stangassinger, L.M.[Lea M.],
Kreutzer, C.[Christina],
Couillard-Després, S.[Sebastien],
Oostingh, G.J.[Gertie J.],
Hittmair, A.[Anton],
Gadermayr, M.[Michael],
Evaluation of Multi-Scale Multiple Instance Learning to Improve
Thyroid Cancer Classification,
IPTA22(1-6)
IEEE DOI
2206
Deep learning, Image resolution, Fluctuations, Training data,
Data visualization, Manuals, Robustness, Histology,
Multi-resolution classification
BibRef
Struski, L.[Lukasz],
Danel, T.[Tomasz],
Smieja, M.[Marek],
Tabor, J.[Jacek],
Zielinski, B.[Bartosz],
SONGs: Self-Organizing Neural Graphs,
WACV23(3837-3846)
IEEE DOI
2302
Training, Deep learning, Pipelines, Neural networks, Directed graphs,
Markov processes, Algorithms: Machine learning architectures,
and algorithms (including transfer)
BibRef
Rymarczyk, D.[Dawid],
Borowa, A.[Adriana],
Tabor, J.[Jacek],
Zielinski, B.[Bartosz],
Kernel Self-Attention for Weakly-supervised Image Classification
using Deep Multiple Instance Learning,
WACV21(1720-1729)
IEEE DOI
2106
Tensors, Databases,
Computational modeling, Supervised learning, Retina
BibRef
Gildenblat, J.[Jacob],
Ben-Shaul, I.[Ido],
Lapp, Z.[Zvi],
Klaiman, E.[Eldad],
Certainty Pooling for Multiple Instance Learning,
AIDP20(141-153).
Springer DOI
2103
BibRef
Wang, K.[Kaili],
Oramas Mogrovejo, J.A.[Jose A.],
Tuytelaars, T.[Tinne],
In Defense of LSTMS for Addressing Multiple Instance Learning Problems,
ACCV20(VI:444-460).
Springer DOI
2103
BibRef
Yuan, L.M.[Li-Ming],
Wen, X.B.[Xian-Bin],
Xu, H.X.[Hai-Xia],
Zhao, L.[Lu],
Multiple-Instance Learning with Empirical Estimation Guided Instance
Selection,
ICPR18(770-775)
IEEE DOI
1812
Training, Prototypes, Estimation, Support vector machines, Standards,
Supervised learning, Reactive power
BibRef
Kandemir, M.[Melih],
Haussmann, M.[Manuel],
Diego, F.[Ferran],
Rajamani, K.[Kumar],
van der Laak, J.[Jeroen],
Hamprecht, F.[Fred],
Variational Weakly Supervised Gaussian Processes,
BMVC16(xx-yy).
HTML Version.
1805
Multiple instance learning (MIL)
BibRef
Dong, M.,
Pang, K.,
Wu, Y.,
Xue, J.H.,
Hospedales, T.M.,
Ogasawara, T.,
Transferring CNNS to multi-instance multi-label classification on
small datasets,
ICIP17(1332-1336)
IEEE DOI
1803
Convolution, Feature extraction, Image annotation, Logistics,
Semantics, Task analysis, Training, CNN, Multi-instance, Multi-label,
Transfer Learning
BibRef
Haußmann, M.,
Hamprecht, F.A.,
Kandemir, M.,
Variational Bayesian Multiple Instance Learning with Gaussian
Processes,
CVPR17(810-819)
IEEE DOI
1711
Gaussian processes, Object detection, Pipelines, Predictive models,
Proposals, Supervised learning, Training
BibRef
Karem, A.,
Frigui, H.,
Multiple Instance Learning with multiple positive and negative target
concepts,
ICPR16(474-479)
IEEE DOI
1705
Clustering algorithms, Correlation, Noise measurement,
Optimization, Standards, Support, vector, machines
BibRef
Venkatesan, R.,
Chandakkar, P.S.,
Li, B.,
Simpler Non-Parametric Methods Provide as Good or Better Results to
Multiple-Instance Learning,
ICCV15(2605-2613)
IEEE DOI
1602
Benchmark testing
BibRef
Wang, X.,
Zhu, Z.,
Yao, C.,
Bai, X.,
Relaxed Multiple-Instance SVM with Application to Object Discovery,
ICCV15(1224-1232)
IEEE DOI
1602
Image edge detection
BibRef
Sikka, K.[Karan],
Giri, R.[Ritwik],
Bartlett, M.[Marian],
Joint Clustering and Classification for Multiple Instance Learning,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Yuan, L.M.[Li-Ming],
Zhao, L.[Lu],
Xu, H.X.[Hai-Xia],
Multi-instance learning via instance-based and bag-based
representation transformations,
ICIP15(2771-2775)
IEEE DOI
1512
Multi-instance learning
BibRef
Rastegari, M.[Mohammad],
Hajishirzi, H.[Hannaneh],
Farhadi, A.[Ali],
Discriminative and consistent similarities in instance-level Multiple
Instance Learning,
CVPR15(740-748)
IEEE DOI
1510
BibRef
Hajimirsadeghi, H.[Hossein],
Yan, W.[Wang],
Vahdat, A.[Arash],
Mori, G.[Greg],
Visual recognition by counting instances:
A multi-instance cardinality potential kernel,
CVPR15(2596-2605)
IEEE DOI
1510
BibRef
Hoffman, J.[Judy],
Pathak, D.[Deepak],
Darrell, T.J.[Trevor J.],
Saenko, K.[Kate],
Detector discovery in the wild:
Joint multiple instance and representation learning,
CVPR15(2883-2891)
IEEE DOI
1510
BibRef
Wu, J.J.[Jia-Jun],
Yu, Y.[Yinan],
Huang, C.[Chang],
Yu, K.[Kai],
Deep multiple instance learning for image classification and
auto-annotation,
CVPR15(3460-3469)
IEEE DOI
1510
BibRef
Li, W.X.[Wei-Xin],
Vasconcelos, N.M.[Nuno M.],
Multiple instance learning for soft bags via top instances,
CVPR15(4277-4285)
IEEE DOI
1510
BibRef
Yoon, J.[Jaesik],
Choi, J.H.[Jin-Ho],
Yoo, C.D.[Chang D.],
A hierarchical-structured dictionary learning for image
classification,
ICIP14(155-159)
IEEE DOI
1502
Algorithm design and analysis
BibRef
Shrivastava, A.[Ashish],
Pillai, J.K.[Jaishanker K.],
Patel, V.M.[Vishal M.],
Chellappa, R.[Rama],
Dictionary-based multiple instance learning,
ICIP14(160-164)
IEEE DOI
1502
Computer vision
BibRef
Fukui, T.[Takayuki],
Wada, T.[Toshikazu],
Commonality Preserving Multiple Instance Clustering Based on Diverse
Density,
FSLCV14(III: 322-335).
Springer DOI
1504
BibRef
Earlier:
Commonality Preserving Image-Set Clustering Based on Diverse Density,
ISVC14(I: 258-269).
Springer DOI
1501
BibRef
Ali, K.[Karim],
Saenko, K.[Kate],
Confidence-Rated Multiple Instance Boosting for Object Detection,
CVPR14(2433-2440)
IEEE DOI
1409
Gradient Boosting; Mutliple Instance Learning; Object Detection
BibRef
Wang, Y.Y.[Ying-Ying],
Zhang, C.[Chun],
Wang, Z.H.[Zhi-Hua],
Rate distortion Multiple Instance Learning for image classification,
ICIP13(3235-3238)
IEEE DOI
1402
Image Classification; Multiple Instance Learning; Rate Distortion
BibRef
Zhao, H.F.[Hai-Feng],
Cheng, J.[Jun],
Jiang, J.[Jun],
Tao, D.C.[Da-Cheng],
Multiple instance learning via distance metric optimization,
ICIP13(2617-2621)
IEEE DOI
1402
MILES
BibRef
Cheplygina, V.[Veronika],
Tax, D.M.J.[David M. J.],
Loog, M.[Marco],
Class-Dependent Dissimilarity Measures for Multiple Instance Learning,
SSSPR12(602-610).
Springer DOI
1211
BibRef
Antic, B.[Borislav],
Ommer, B.[Björn],
Robust Multiple-Instance Learning with Superbags,
ACCV12(II:242-255).
Springer DOI
1304
Alternate between learn classifier with missing labels,
learn missing labels with a classifier.
BibRef
Brossi, S.D.,
Bradley, A.P.,
A Comparison of Multiple Instance and Group Based Learning,
DICTA12(1-8).
IEEE DOI
1303
BibRef
Ngo, T.D.[Thanh Duc],
Le, D.D.[Duy-Dinh],
Satoh, S.[Shin'ichi],
Improving Image Categorization by Using Multiple Instance Learning with
Spatial Relation,
CIAP11(I: 108-117).
Springer DOI
1109
BibRef
Kang, F.[Feng],
Naphade, M.R.[Milind R.],
A Generalized Multiple Instance Learning Algorithm for Iterative
Distillation and Cross-Granular Propagation of Video Annotations,
ICIP07(II: 205-208).
IEEE DOI
0709
BibRef
Earlier:
A Generalized Multiple Instance Learning Algorithm with Multiple
Selection Strategies for Cross Granular Learning,
ICIP06(3213-3216).
IEEE DOI
0610
BibRef
Du, R.[Ruo],
Wang, S.[Sheng],
Wu, Q.A.[Qi-Ang],
He, X.J.[Xiang-Jian],
Learn Concepts in Multiple-Instance Learning with Diverse Density
Framework Using Supervised Mean Shift,
DICTA10(643-648).
IEEE DOI
1012
BibRef
Wu, D.[Dijia],
Boyer, K.L.[Kim L.],
Resilient Subclass Discriminant Analysis,
ICCV09(389-396).
IEEE DOI
0909
BibRef
Wu, D.[Dijia],
Bi, J.[Jinbo],
Boyer, K.L.[Kim L.],
A min-max framework of cascaded classifier with multiple instance
learning for computer aided diagnosis,
CVPR09(1359-1366).
IEEE DOI
0906
BibRef
Pao, H.T.[Hsiao T.],
Xu, Y.Y.[Yeong Y.],
Chuang, S.C.[Shun C.],
Fu, H.C.[Hsin C.],
Image Classification and Indexing by EM Based Multiple-Instance
Learning,
Visual07(146-153).
Springer DOI
0706
BibRef
Maron, O.[Oded],
Ratan, A.L.[Aparna L.],
Multiple-Instance Learning for Natural Scene Classification,
DARPA98(1031-1036).
BibRef
9800
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Learning Model Descriptions .