14.3.3 Boosting, AdaBoost Technique

Freund, Y., Schapire, R.E.,
A decision-theoretic generalization of on-line learning and an application to boosting,
JCSSI(55), No. 1, 1997, pp. 119-139. BibRef 9700

Schapire, R.E., Singer, Y.,
Improved boosting algorithms using confidence-rated predictions,
MachLearn(37), No. 3, December 1999, pp. 297-336.
DOI Link Improvements where hypotheses may assign confidences to each of their predictions. BibRef 9912

Frossyniotis, D., Likas, A.C., Stafylopatis, A.,
A clustering method based on boosting,
PRL(25), No. 6, 19 April 2004, pp. 641-654.
Elsevier DOI 0405
At each boosting iteration, a new training set is created using weighted random sampling from the original dataset. Then apply clustering. BibRef

Tao, Q.[Qing], Wu, G.W.[Gao-Wei], Wang, J.[Jue],
A generalized S-K algorithm for learning [nu]-SVM classifiers,
PRL(25), No. 10, 16 July 2004, pp. 1165-1171.
Elsevier DOI 0407
BibRef

Tao, Q.[Qing], Wu, G.W.[Gao-Wei], Wang, J.[Jue],
A new maximum margin algorithm for one-class problems and its boosting implementation,
PR(38), No. 7, July 2005, pp. 1071-1077.
Elsevier DOI 0505
BibRef

Tao, Q.[Qing], Wu, G.W.[Gao-Wei], Wang, J.[Jue],
A general soft method for learning SVM classifiers with L1-norm penalty,
PR(41), No. 3, March 2008, pp. 939-948.
Elsevier DOI 0711
Support vector machines; Classification; [nu]-SVMs; Nearest points; Gilbert's algorithms; Schlesinger-Kozinec's algorithms; Mitchell-Dem'yanov-Malozemov's algorithms; Soft convex hulls BibRef

Chen, S., Wang, X.X., Harris, C.J.,
Experiments With Repeating Weighted Boosting Search for Optimization in Signal Processing Applications,
SMC-B(35), No. 4, August 2005, pp. 682-693.
IEEE DOI 0508
BibRef

Yin, X.C.[Xu-Cheng], Liu, C.P.[Chang-Ping], Han, Z.[Zhi],
Feature combination using boosting,
PRL(26), No. 14, 15 October 2005, pp. 2195-2205.
Elsevier DOI 0510
BibRef

Nishii, R., Eguchi, S.,
Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods,
GeoRS(43), No. 11, November 2005, pp. 2547-2554.
IEEE DOI 0512
BibRef

Kawaguchi, S., Nishii, R.,
Hyperspectral Image Classification by Bootstrap AdaBoost With Random Decision Stumps,
GeoRS(45), No. 11, November 2007, pp. 3845-3851.
IEEE DOI 0709
BibRef

Opelt, A.[Andreas], Pinz, A.[Axel], Fussenegger, M.[Michael], Auer, P.[Peter],
Generic Object Recognition with Boosting,
PAMI(28), No. 3, March 2006, pp. 416-431.
IEEE DOI 0602
BibRef
Earlier: A1, A3, A2, A4:
Weak Hypotheses and Boosting for Generic Object Detection and Recognition,
ECCV04(Vol II: 71-84).
Springer DOI 0405
Extract local regions, use local descriptors. Use boosting on the feature vectors. Use boosting to combine features. This allows for using diverse features. Weekly supervised. BibRef

Opelt, A.[Andreas], Pinz, A.[Axel],
Object Localization with Boosting and Weak Supervision for Generic Object Recognition,
SCIA05(862-871).
Springer DOI 0506
BibRef

Fussenegger, M., Opelt, A., Pinz, A., Auer, P.,
Object recognition using segmentation for feature detection,
ICPR04(III: 41-44).
IEEE DOI 0409
BibRef

Fussenegger, M.[Michael], Opelt, A.[Andreas], Pinz, A.[Axel],
Object localization/segmentation using generic shape priors,
ICPR06(IV: 41-44).
IEEE DOI 0609
BibRef

Opelt, A.[Andreas], Pinz, A.[Axel], Zisserman, A.[Andrew],
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection,
IJCV(80), No. 1, October 2008, pp. xx-yy.
Springer DOI 0809
BibRef
Earlier:
Incremental learning of object detectors using a visual shape alphabet,
CVPR06(I: 3-10).
IEEE DOI 0606
Award, CVPR, HM. BibRef
And:
A Boundary-Fragment-Model for Object Detection,
ECCV06(II: 575-588).
Springer DOI 0608
BibRef

Wang, X.[Xiao], Wang, H.[Han],
Classification by evolutionary ensembles,
PR(39), No. 4, April 2006, pp. 595-607.
Elsevier DOI 0604
Multiple classifier system; Genetic algorithms; Evolutionary learning; Classifier combination; AdaBoost; Bagging BibRef

McDonald, R.A.[Ross A.],
The mean subjective utility score, a novel metric for cost-sensitive classifier evaluation,
PRL(27), No. 13, 1 October 2006, pp. 1472-1477.
Elsevier DOI 0606
Misclassification costs are not all equal. Cost-sensitivity; Cost matrix; Utility; Decision theory; Boosting BibRef

Sun, Y.J.[Yi-Jun], Todorovic, S.[Sinisa], Li, J.[Jian],
Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework,
PRL(28), No. 5, 1 April 2007, pp. 631-643.
Elsevier DOI 0703
AdaBoost; Margin theory; Multi-class classification problem BibRef

Le, D.D.[Duy-Dinh], Satoh, S.[Shin'ichi],
Ent-Boost: Boosting Using Entropy Measures for Robust Object Detection,
PRL(28), No. 9, 1 July 2007, pp. 1083-1090.
Elsevier DOI 0704
BibRef
Earlier: ICPR06(II: 602-605).
IEEE DOI 0609
Real AdaBoost; Information theory; Entropy; Minimum description length principle (MDLP); Variable discretization; Object detection BibRef

Pham, T.V.[Thang V.], Smeulders, A.W.M.[Arnold W.M.],
Quadratic boosting,
PR(41), No. 1, January 2008, pp. 331-341.
Elsevier DOI 0710
BibRef
Earlier:
Metric tree partitioning and Taylor approximation for fast support vector classification,
ICPR06(IV: 132-135).
IEEE DOI 0609
AdaBoost; Boosting algorithm; Coordinate descent; Generalization error; Object detection; Quadratic boosting; Randomized relabeling; VC-dimension BibRef

Verschae, R.[Rodrigo], Ruiz-del-Solar, J.[Javier], Correa, M.[Mauricio],
A unified learning framework for object detection and classification using nested cascades of boosted classifiers,
MVA(19), No. 2, March 2008, pp. 85-103.
Springer DOI 0802
BibRef

Rodriguez, J.J.[Juan J.], Maudes, J.[Jesus],
Boosting recombined weak classifiers,
PRL(29), No. 8, 1 June 2008, pp. 1049-1059.
Elsevier DOI 0804
Boosting; Classifier ensembles; Decision stumps BibRef

Zhang, C.X.[Chun-Xia], Zhang, J.S.[Jiang-She],
RotBoost: A technique for combining Rotation Forest and AdaBoost,
PRL(29), No. 10, 15 July 2008, pp. 1524-1536.
Elsevier DOI 0711
Ensemble method; Base learning algorithm; AdaBoost; Rotation Forest; Bagging; MultiBoost BibRef

Furst, L.[Luka], Fidler, S.[Sanja], Leonardis, A.[Ales],
Selecting features for object detection using an Adaboost-compatible evaluation function,
PRL(29), No. 11, 1 August 2008, pp. 1603-1612.
Elsevier DOI 0804
Feature selection; AdaBoost; Object detection BibRef

Fleuret, F.[Francois],
Multi-layer boosting for pattern recognition,
PRL(30), No. 3, 1 February 2009, pp. 237-241.
Elsevier DOI 0804
Boosting; Multi-layer perceptron; Functional gradient descent; Convolutional network BibRef

Šochman, J.[Jan], Matas, J.G.[Jirí G.],
Learning Fast Emulators of Binary Decision Processes,
IJCV(83), No. 2, June 2009, pp. xx-yy.
Springer DOI 0903
BibRef
Earlier:
Learning a Fast Emulator of a Binary Decision Process,
ACCV07(II: 236-245).
Springer DOI 0711
BibRef
Earlier:
WaldBoost: Learning for Time Constrained Sequential Detection,
CVPR05(II: 150-156).
IEEE DOI 0507
BibRef
Earlier:
Inter-stage feature propagation in cascade building with adaboost,
ICPR04(I: 236-239).
IEEE DOI 0409
BibRef

Lu, Y.J.[Yi-Juan], Tian, Q.[Qi],
Discriminant Subspace Analysis: An Adaptive Approach for Image Classification,
MultMed(11), No. 7, November 2009, pp. 1289-1300.
IEEE DOI 0911
BibRef

Lu, Y.J.[Yi-Juan], Tian, Q.[Qi], Huan, T.S.[Thomas S.],
Interactive Boosting for Image Classification,
MCAM07(315-324).
Springer DOI 0706
BibRef

Gao, C.X.[Chang-Xin], Sang, N.[Nong], Tang, Q.L.[Qi-Ling],
On selection and combination of weak learners in AdaBoost,
PRL(31), No. 9, 1 July 2010, pp. 991-1001.
Elsevier DOI 1004
Adaboost algorithm; Distance related criterion; Kernel-based perceptron BibRef

Chen, K.T.[Kuan-Ting], Lin, K.H.[Kuan-Hung], Kuo, Y.H.[Yin-Hsi], Wu, Y.L.[Yi-Lun], Hsu, W.H.[Winston H.],
Boosting image object retrieval and indexing by automatically discovered pseudo-objects,
JVCIR(21), No. 8, November 2010, pp. 815-825.
Elsevier DOI 1011
Image retrieval; Object retrieval; Pseudo-object; Visual word; Local feature; Bundle feature; Indexing; Large-scale BibRef

Lin, K.H.[Kuan-Hung], Chen, K.T.[Kuan-Ting], Hsu, W.H.[Winston H.], Lee, C.J.[Chun-Jen], Li, T.H.[Tien-Hsu],
Boosting object retrieval by estimating pseudo-objects,
ICIP09(785-788).
IEEE DOI 0911
BibRef

Shen, C.H.[Chun-Hua], Li, H.X.[Han-Xi],
On the Dual Formulation of Boosting Algorithms,
PAMI(32), No. 12, December 2010, pp. 2216-2231.
IEEE DOI 1011
BibRef
Earlier: A2, A1:
Boosting the Minimum Margin: LPBoost vs. AdaBoost,
DICTA08(533-539).
IEEE DOI 0812
BibRef

Shen, C.H.[Chun-Hua], Wang, P.[Peng], Li, H.X.[Han-Xi],
LACBoost and FisherBoost: Optimally Building Cascade Classifiers,
ECCV10(II: 608-621).
Springer DOI 1009
BibRef

Chen, S.[Shi], Wang, J.Q.[Jin-Qiao], Ouyang, Y.[Yi], Wang, B.[Bo], Xu, C.S.[Chang-Sheng], Lu, H.Q.[Han-Qing],
Boosting part-sense multi-feature learners toward effective object detection,
CVIU(115), No. 3, March 2011, pp. 364-374.
Elsevier DOI 1103
AdaBoost; Object detection; Multi-feature learners; L1-regularized gradient boosting BibRef

Wang, P.[Peng], Shen, C.H.[Chun-Hua], Barnes, N.M.[Nick M.], Zheng, H.[Hong], Ren, Z.[Zhang],
Asymmetric Totally-Corrective Boosting for Real-Time Object Detection,
ACCV10(I: 176-188).
Springer DOI 1011
BibRef

Hao, Z.H.[Zhi-Hui], Shen, C.H.[Chun-Hua], Barnes, N.M.[Nick M.], Wang, B.[Bo],
Totally-Corrective Multi-class Boosting,
ACCV10(IV: 269-280).
Springer DOI 1011
BibRef

Shen, C.H.[Chun-Hua], Hao, Z.H.[Zhi-Hui],
A direct formulation for totally-corrective multi-class boosting,
CVPR11(2585-2592).
IEEE DOI 1106
BibRef

Zhou, J.[Jun], Fu, Z.Y.[Zhou-Yu], Robles-Kelly, A.[Antonio],
Structured learning approach to image descriptor combination,
IET-CV(5), No. 2, 2011, pp. 134-142.
DOI Link 1103
BibRef
Earlier:
Learning the Optimal Transformation of Salient Features for Image Classification,
DICTA09(125-131).
IEEE DOI 0912
Combine descriptors. BibRef

Fu, Z.Y.[Zhou-Yu], Caelli, T.M.[Terry M.], Liu, N.J.[Nian-Jun], Robles-Kelly, A.[Antonio],
Boosted Band Ratio Feature Selection for Hyperspectral Image Classification,
ICPR06(I: 1059-1062).
IEEE DOI 0609
BibRef

Zhang, K.[Ke], Jin, H.D.[Hui-Dong], Fu, Z.Y.[Zhou-Yu], Liu, N.J.[Nian-Jun],
Optimal Learning High-Order Markov Random Fields Priors of Colour Image,
ACCV07(I: 482-491).
Springer DOI 0711
BibRef

Landesa-Vázquez, I.[Iago], Alba-Castro, J.L.[José Luis],
Shedding light on the asymmetric learning capability of AdaBoost,
PRL(33), No. 3, 1 February 2012, pp. 247-255.
Elsevier DOI 1201
AdaBoost; Asymmetry; Boosting; Classification; Cost BibRef

Shen, C.H.[Chun-Hua], Wang, P.[Peng], Shen, F.M.[Fu-Min], Wang, H.Z.[Han-Zi],
U_Boost: Boosting with the Universum,
PAMI(34), No. 4, April 2012, pp. 825-832.
IEEE DOI 1203
BibRef

Ribeiro, P.C.[Pedro Canotilho], Moreno, P.[Plinio], Santos-Victor, J.[José],
Introducing fuzzy decision stumps in boosting through the notion of neighbourhood,
IET-CV(6), No. 3, 2012, pp. 214-223.
DOI Link 1205
BibRef
Earlier: A2, A1, A3:
Feature Set Search Space for FuzzyBoost Learning,
IbPRIA11(248-255).
Springer DOI 1106

See also Feature Selection for Tracker-Less Human Activity Recognition. BibRef

Cao, J.J.[Jing-Jing], Kwong, S.[Sam], Wang, R.[Ran],
A noise-detection based AdaBoost algorithm for mislabeled data,
PR(45), No. 12, December 2012, pp. 4451-4465.
Elsevier DOI 1208
Pattern recognition; Ensemble learning; AdaBoost; k-NN; EM BibRef

Saberian, M.J.[Mohammad J.], Vasconcelos, N.M.[Nuno M.],
Learning Optimal Embedded Cascades,
PAMI(34), No. 10, October 2012, pp. 2005-2018.
IEEE DOI 1208
BibRef
Earlier:
Boosting algorithms for simultaneous feature extraction and selection,
CVPR12(2448-2455).
IEEE DOI 1208
BibRef

Saberian, M.J.[Mohammad J.], Masnadi-Shirazi, H.[Hamed], Vasconcelos, N.M.[Nuno M.],
TaylorBoost: First and second-order boosting algorithms with explicit margin control,
CVPR11(2929-2934).
IEEE DOI 1106
BibRef

Moghimi, M.[Mohammad], Belongie, S.J.[Serge J.], Saberian, M.J.[Mohammad J.], Yang, J.[Jian], Vasconcelos, N.M.[Nuno M.], Li, L.J.[Li-Jia],
Boosted Convolutional Neural Networks,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Zheng, W.[Wei], Liang, L.[Luhong], Chang, H.[Hong], Heng, C.K.[Cher-Keng], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Boosted translation-tolerable classifiers for fast object detection,
IVC(30), No. 8, August 2012, pp. 480-491.
Elsevier DOI 1209
Object detection; Boosting; Maximal Translation-Tolerable Region (MTTR); Granularity-Adaptively-Tunable (GAT) BibRef

Shen, C.H.[Chun-Hua], Wang, P.[Peng], Paisitkriangkrai, S.[Sakrapee], van den Hengel, A.J.[Anton J.],
Training Effective Node Classifiers for Cascade Classification,
IJCV(103), No. 3, July 2013, pp. 326-347.
Springer DOI 1306
BibRef

Paisitkriangkrai, S.[Sakrapee], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Asymmetric Pruning for Learning Cascade Detectors,
MultMed(16), No. 5, August 2014, pp. 1254-1267.
IEEE DOI 1410
face recognition BibRef

Paisitkriangkrai, S.[Sakrapee], Wu, L.[Lin], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Structured learning of metric ensembles with application to person re-identification,
CVIU(156), No. 1, 2017, pp. 51-65.
Elsevier DOI 1702
BibRef
Earlier: A1, A3, A4, Only:
Learning to rank in person re-identification with metric ensembles,
CVPR15(1846-1855)
IEEE DOI 1510
Person re-identification BibRef

Shen, C.H.[Chun-Hua], Lin, G.S.[Guo-Sheng], van den Hengel, A.J.[Anton J.],
StructBoost: Boosting Methods for Predicting Structured Output Variables,
PAMI(36), No. 10, October 2014, pp. 2089-2103.
IEEE DOI 1410
computer vision BibRef

Shi, Q.F.[Qin-Feng], Reid, M., Caetano, T.S.[Tibério S.], van den Hengel, A.J.[Anton J.], Wang, Z.,
A Hybrid Loss for Multiclass and Structured Prediction,
PAMI(37), No. 1, January 2015, pp. 2-12.
IEEE DOI 1412
FCC BibRef

Lin, G.S.[Guo-Sheng], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.], Suter, D.[David],
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization,
ACCV12(II:782-795).
Springer DOI 1304
BibRef

Liu, X.[Xi], Shi, Z.P.[Zhi-Ping], Shi, Z.Z.[Zhong-Zhi],
A co-boost framework for learning object categories from Google Images with 1st and 2nd order features,
VC(30), No. 1, January 2014, pp. 5-17.
WWW Link. 1402
BibRef
Earlier:
Filter object categories using CoBoost with 1st and 2nd order features,
ICIP09(309-312).
IEEE DOI 0911
BibRef

Fernández-Baldera, A.[Antonio], Baumela, L.[Luis],
Multi-class boosting with asymmetric binary weak-learners,
PR(47), No. 5, 2014, pp. 2080-2090.
Elsevier DOI 1402
AdaBoost BibRef

Tanha, J.[Jafar], van Someren, M.[Maarten], Afsarmanesh, H.[Hamideh],
Boosting for multiclass semi-supervised learning,
PRL(37), No. 1, 2014, pp. 63-77.
Elsevier DOI 1402
Semi-supervised learning BibRef

Wu, S., Nagahashi, H.,
Parameterized AdaBoost: Introducing a Parameter to Speed Up the Training of Real AdaBoost,
SPLetters(21), No. 6, June 2014, pp. 687-691.
IEEE DOI 1404
Indexes BibRef

Xu, J.S.[Jing-Song], Wu, Q.A.[Qi-Ang], Zhang, J.[Jian], Tang, Z.M.[Zhen-Min],
Exploiting Universum data in AdaBoost using gradient descent,
IVC(32), No. 8, 2014, pp. 550-557.
Elsevier DOI 1407
AdaBoost BibRef

Zhang, L., Gao, Y., Hong, C., Feng, Y., Zhu, J., Cai, D.,
Feature Correlation Hypergraph: Exploiting High-order Potentials for Multimodal Recognition,
Cyber(44), No. 8, August 2014, pp. 1408-1419.
IEEE DOI 1407
Boosting BibRef

Tokarczyk, P., Wegner, J.D., Walk, S., Schindler, K.,
Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images,
GeoRS(53), No. 1, January 2015, pp. 280-295.
IEEE DOI 1410
feature extraction BibRef

Nie, Q.F.[Qing-Feng], Jin, L.[Lizuo], Fei, S.[Shumin],
Probability estimation for multi-class classification using AdaBoost,
PR(47), No. 12, 2014, pp. 3931-3940.
Elsevier DOI 1410
AdaBoost BibRef

Yu, Z.W.[Zhi-Wen], Li, L.[Le], Liu, J.M.[Ji-Ming], Han, G.Q.[Guo-Qiang],
Hybrid Adaptive Classifier Ensemble,
Cyber(45), No. 2, February 2015, pp. 177-190.
IEEE DOI 1502
biology computing BibRef

Li, L.[Le], Yu, Z.W.[Zhi-Wen], Liu, J.M.[Ji-Ming], You, J.[Jane], Wong, H.S.[Hau-San], Han, G.Q.[Guo-Qiang],
Multi-view Based AdaBoost Classifier Ensemble for Class Prediction from Gene Expression Profiles,
ICPR14(178-183)
IEEE DOI 1412
Accuracy BibRef

Zhang, Z.M.[Zi-Ming], Torr, P.H.S.[Philip H. S.],
Object Proposal Generation Using Two-Stage Cascade SVMs,
PAMI(38), No. 1, January 2016, pp. 102-115.
IEEE DOI 1601
Calibration BibRef

Zhang, Z.M.[Zi-Ming], Warrell, J.[Jonathan], Torr, P.H.S.[Philip H. S.],
Proposal generation for object detection using cascaded ranking SVMs,
CVPR11(1497-1504).
IEEE DOI 1106
BibRef

Warrell, J.[Jonathan], Torr, P.H.S.[Philip H. S.],
Multiple-Instance Learning with Structured Bag Models,
EMMCVPR11(369-384).
Springer DOI 1107
BibRef

Warrell, J.[Jonathan], Torr, P.H.S.[Philip H.S.], Prince, S.J.D.[Simon J.D.],
Styp-boost: A Bilinear Boosting Algorithm for Learning Style-parameterized Classifiers,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Ahachad, A.[Anas], Álvarez-Pérez, L.[Lorena], Figueiras-Vidal, A.R.[Aníbal R.],
Boosting ensembles with controlled emphasis intensity,
PRL(88), No. 1, 2017, pp. 1-5.
Elsevier DOI 1703
Boosting BibRef

Yin, H.B.[Hai-Bo], Yang, J.A.[Jun-An], Wang, W.[Wei], Liu, H.[Hui],
Set-Based Boosting for Instance-Level Transfer on Multi-Classification,
IEICE(E100-D), No. 5, May 2017, pp. 1079-1086.
WWW Link. 1705
BibRef

Hajimirsadeghi, H.[Hossein], Mori, G.[Greg],
Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks,
PAMI(39), No. 9, September 2017, pp. 1839-1852.
IEEE DOI 1708
Approximation algorithms, Feature extraction, Inference algorithms, Markov random fields, Standards, Support vector machines, Training, Markov network, Multiple instance learning, cardinality models, conditional, random, field BibRef

Hajimirsadeghi, H.[Hossein], Mori, G.[Greg],
Learning Ensembles of Potential Functions for Structured Prediction with Latent Variables,
ICCV15(4059-4067)
IEEE DOI 1602
BibRef
And:
Multiple instance real boosting with aggregation functions,
ICPR12(2706-2710).
WWW Link. 1302
Boosting BibRef

Li, F.F.[Feng-Fu], Qiao, H.[Hong], Zhang, B.[Bo],
Discriminatively boosted image clustering with fully convolutional auto-encoders,
PR(83), 2018, pp. 161-173.
Elsevier DOI 1808
Image clustering, Fully convolutional auto-encoder, Representation learning, Discriminatively boosted clustering BibRef

Sabzevari, M.[Maryam], Martínez-Muñoz, G.[Gonzalo], Suárez, A.[Alberto],
Vote-boosting ensembles,
PR(83), 2018, pp. 119-133.
Elsevier DOI 1808
Ensemble learning, Boosting, Uncertainty-based emphasis, Robust classification BibRef

Ding, C.[Chen], Li, Y.[Yu], Wen, Y.[Yue], Zheng, M.M.[Meng-Meng], Zhang, L.[Lei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning],
Boosting Few-Shot Hyperspectral Image Classification Using Pseudo-Label Learning,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhou, F.[Fei], Wei, W.[Wei], Zhang, L.[Lei], Zhang, Y.N.[Yan-Ning],
Learning to Class-Adaptively Manipulate Embeddings for Few-Shot Learning,
CirSysVideo(33), No. 9, September 2023, pp. 5062-5075.
IEEE DOI 2310
BibRef

Ding, C.[Chen], Zheng, M.M.[Meng-Meng], Chen, F.X.[Fei-Xiong], Zhang, Y.K.[Yuan-Kun], Zhuang, X.[Xusi], Fan, E.[Enquan], Wen, D.[Dushi], Zhang, L.[Lei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning],
Hyperspectral Image Classification Promotion Using Clustering Inspired Active Learning,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wei, W.[Wei], Xu, S.Z.[Song-Zheng], Zhang, L.[Lei], Zhang, J.Y.[Jin-Yang], Zhang, Y.N.[Yan-Ning],
Boosting Hyperspectral Image Classification With Unsupervised Feature Learning,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Feature extraction, Task analysis, Noise measurement, Data mining, Training, Support vector machines, Hyperspectral imaging, semisupervised learning BibRef

Wang, Y.C.[Yun-Cheng], Ge, X.[Xiou], Wang, B.[Bin], Kuo, C.C.J.[C.C. Jay],
KGBoost: A classification-based knowledge base completion method with negative sampling,
PRL(157), 2022, pp. 104-111.
Elsevier DOI 2205
Knowledge base completion, Negative sampling, Binary classification, XGBoost Classifiers BibRef

Patel, A.M.[Alpesh M.], Suthar, A.[Anil],
AdaBoosted Extra Trees Classifier for Object-Based Multispectral Image Classification of Urban Fringe Area,
IJIG(22), No. 3 2022, pp. 2140006.
DOI Link 2206
BibRef

Sigrist, F.[Fabio],
Latent Gaussian Model Boosting,
PAMI(45), No. 2, February 2023, pp. 1894-1905.
IEEE DOI 2301
Predictive models, Data models, Boosting, Machine learning, Analytical models, Splines (mathematics), Spatial databases, gaussian processes BibRef

Zhao, C.M.[Chang-Ming], Wu, D.R.[Dong-Rui], Huang, J.[Jian], Yuan, Y.[Ye], Zhang, H.T.[Hai-Tao], Peng, R.M.[Rui-Min], Shi, Z.H.[Zhen-Hua],
BoostTree and BoostForest for Ensemble Learning,
PAMI(45), No. 7, July 2023, pp. 8110-8126.
IEEE DOI 2306
Boosting, Bagging, Regression tree analysis, Biological system modeling, Predictive models, Ensemble learning, ensemble learning BibRef

Chen, Y.H.[Yu-Hao], Tan, X.[Xin], Zhao, B.[Borui], Chen, Z.W.[Zhao-Wei], Song, R.J.[Ren-Jie], Liang, J.J.[Jia-Jun], Lu, X.Q.[Xue-Quan],
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data,
CVPR23(7548-7557)
IEEE DOI 2309
BibRef

Qiu, B.[Benliu], Li, H.L.[Hong-Liang], Wen, H.T.[Hai-Tao], Qiu, H.Q.[He-Qian], Wang, L.X.[Lan-Xiao], Meng, F.M.[Fan-Man], Wu, Q.B.[Qing-Bo], Pan, L.[Lili],
CafeBoost: Causal Feature Boost to Eliminate Task-Induced Bias for Class Incremental Learning,
CVPR23(16016-16025)
IEEE DOI 2309
BibRef

Zeng, Q.J.[Qing-Jie], Xie, Y.T.[Yu-Tong], Lu, Z.[Zilin], Xia, Y.[Yong],
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training,
CVPR23(15671-15680)
IEEE DOI 2309
BibRef

Zhang, Z.D.[Zhao-Di], Xue, Z.Y.[Zhi-Yi], Chen, Y.[Yang], Liu, S.[Si], Zhang, Y.L.[Yue-Ling], Liu, J.[Jing], Zhang, M.[Min],
Boosting Verified Training for Robust Image Classifications via Abstraction,
CVPR23(16251-16260)
IEEE DOI 2309
BibRef

Ali, H.A.[Hanae Aoulad], Mohamed, C.[Chrayah], Abdelhamid, B.[Bouzidi], Ourdani, N.[Nabil], El Alami, T.[Taha],
A Comparative Evaluation use Bagging and Boosting Ensemble Classifiers,
ISCV22(1-6)
IEEE DOI 2208
Education, Predictive models, Boosting, Prediction algorithms, Internet, Time factors, Classification, Machine learning BibRef

Gabidolla, M.[Magzhan], Carreira-Perpiñán, M.Á.[Miguel Á.],
Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees,
CVPR22(285-294)
IEEE DOI 2210
Computational modeling, Forestry, Boosting, Linear programming, Partitioning algorithms, Machine learning, Statistical methods BibRef

Zharmagambetov, A.[Arman], Gabidolla, M.[Magzhan], Carreira-Perpiñán, M.Á.[Miguel Á.],
Improved Multiclass Adaboost for Image Classification: The Role of Tree Optimization,
ICIP21(424-428)
IEEE DOI 2201
Training, Image recognition, Scalability, Signal processing algorithms, Signal processing, Boosting, tree optimization BibRef

Qiu, Z.F.[Zhao-Fan], Yao, T.[Ting], Ngo, C.W.[Chong-Wah], Zhang, X.P.[Xiao-Ping], Wu, D.[Dong], Mei, T.[Tao],
Boosting Video Representation Learning with Multi-Faceted Integration,
CVPR21(14025-14034)
IEEE DOI 2111
Training, Aggregates, Semantics, Feature extraction, Boosting BibRef

Chen, Y.Y.[Ying-Yi], Shen, X.[Xi], Hu, S.X.[Shell Xu], Suykens, J.A.K.[Johan A. K.],
Boosting Co-teaching with Compression Regularization for Label Noise,
LLID21(2682-2686)
IEEE DOI 2109
Adaptation models, Image coding, Neural networks, Data compression, Information retrieval BibRef

Singh, P., Mazumder, P., Namboodiri, V.P.,
Accuracy Booster: Performance Boosting using Feature Map Re-calibration,
WACV20(873-882)
IEEE DOI 2006
Complexity theory, Convolution, Computational modeling, Task analysis, Memory management, Boosting BibRef

Antunes, J.[João], Bernardino, A.[Alexandre], Smailagic, A.[Asim], Siewiorek, D.[Daniel],
Weighted Multisource Tradaboost,
IbPRIA19(I:194-205).
Springer DOI 1910
BibRef

Meijer, D.W.J., Tax, D.M.J.,
Regularizing AdaBoost with validation sets of increasing size,
ICPR16(192-197)
IEEE DOI 1705
Bars, Iterative methods, Neural networks, Noise measurement, Standards, Training, Training data, AdaBoost, Ensemble learning, Regularization, Supervised, learning BibRef

Allende-Cid, H.[Héctor], Acuña, D.[Diego], Allende, H.[Héctor],
Subsampling the Concurrent AdaBoost Algorithm: An Efficient Approach for Large Datasets,
CIARP16(318-325).
Springer DOI 1703
BibRef

Lahiri, A., Biswas, P.K.,
A scalable model for knowledge sharing based supervised learning using AdaBoost,
ICAPR15(1-6)
IEEE DOI 1511
digital simulation BibRef

Lahiri, A.[Avisek], Biswas, P.K.[Prabir Kumar],
A New Framework for Multiclass Classification Using Multiview Assisted Adaptive Boosting,
ACCV14(III: 128-143).
Springer DOI 1504
BibRef

Wang, Y.T.[Yu-Ting], Gu, Y.F.[Yan-Feng], Gao, G.M.[Guo-Ming], Wang, Q.W.[Qing-Wang],
Hyperspectral image classification with multiple kernel Boosting algorithm,
ICIP14(5047-5051)
IEEE DOI 1502
Boosting BibRef

Wang, S.X.[Shi-Xun], Pan, P.[Peng], Lu, Y.S.[Yan-Sheng], Jiang, S.[Sheng],
Multiclass Boosting Framework for Multimodal Data Analysis,
MMMod15(II: 560-571).
Springer DOI 1501
BibRef

Hall, D.[David], Perona, P.[Pietro],
From Categories to Individuals in Real Time: A Unified Boosting Approach,
CVPR14(176-183)
IEEE DOI 1409
BibRef

Lin, G.S.[Guo-Sheng], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Approximate constraint generation for efficient structured boosting,
ICIP13(4287-4291)
IEEE DOI 1402
BibRef

Wang, S.[Sheng], Wu, Q.A.[Qi-Ang], He, X.J.[Xiang-Jian], Xu, M.[Min],
On splitting dataset: Boosting Locally Adaptive Regression Kernels for car localization,
ICARCV12(1154-1159).
IEEE DOI 1304
BibRef

Chen, J.X.[Ji-Xu], Liu, X.M.[Xiao-Ming], Lyu, S.W.[Si-Wei],
Boosting with Side Information,
ACCV12(I:563-577).
Springer DOI 1304
BibRef

Li, C.L.[Cheng-Liang], Wang, Z.S.[Zhong-Sheng], Bu, S.H.[Shu-Hui], Liu, Z.B.[Zhen-Bao],
Semi-supervised adaptive parzen Gentleboost algorithm for fault diagnosis,
ICPR12(2290-2293).
WWW Link. 1302
BibRef

Abouelenien, M.[Mohamed], Yuan, X.H.[Xiao-Hui],
SampleBoost: Improving boosting performance by destabilizing weak learners based on weighted error analysis,
ICPR12(585-588).
WWW Link. 1302
BibRef

Ormeño, P.[Pablo], Ramírez, F.[Felipe], Valle, C.[Carlos], Allende-Cid, H.[Héctor], Allende, H.[Héctor],
Robust Asymmetric Adaboost,
CIARP12(519-526).
Springer DOI 1209
BibRef

Merjildo, D.A.F.[Diego Alonso Fernández], Ling, L.L.[Lee Luan],
Enhancing the Performance of Adaboost Algorithms by Introducing a Frequency Counting Factor for Weight Distribution Updating,
CIARP12(527-534).
Springer DOI 1209
BibRef

Borji, A.[Ali],
Boosting bottom-up and top-down visual features for saliency estimation,
CVPR12(438-445).
IEEE DOI 1208
BibRef

Paisitkriangkrai, S.[Sakrapee], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Sharing features in multi-class boosting via group sparsity,
CVPR12(2128-2135).
IEEE DOI 1208
BibRef

Wang, T.[Tao], He, X.M.[Xu-Ming], Shen, C.H.[Chun-Hua], Barnes, N.M.,
Laplacian Margin Distribution Boosting for Learning from Sparsely Labeled Data,
DICTA11(209-216).
IEEE DOI 1205
BibRef

Zheng, S.F.[Song-Feng],
Labeling Image Patches by Boosting based Median Classifier,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Bi, J.[Jinbo], Wu, D.[Dijia], Lu, L.[Le], Liu, M.Z.[Mei-Zhu], Tao, Y.[Yimo], Wolf, M.[Matthias],
AdaBoost on low-rank PSD matrices for metric learning,
CVPR11(2617-2624).
IEEE DOI 1106
BibRef

Ehlers, A.[Arne], Baumann, F.[Florian], Rosenhahn, B.[Bodo],
Boosted Fractal Integral Paths for Object Detection,
ISVC14(II: 458-470).
Springer DOI 1501
BibRef
And:
Exploiting Object Characteristics Using Custom Features for Boosting-Based Classification,
SCIA13(420-431).
Springer DOI 1311
BibRef

Ehlers, A.[Arne], Baumann, F.[Florian], Spindler, R.[Ralf], Glasmacher, B.[Birgit], Rosenhahn, B.[Bodo],
PCA Enhanced Training Data for Adaboost,
CAIP11(I: 410-419).
Springer DOI 1109
BibRef

Baumann, F.[Florian], Ernst, K.[Katharina], Ehlers, A.[Arne], Rosenhahn, B.[Bodo],
Symmetry Enhanced Adaboost,
ISVC10(I: 286-295).
Springer DOI 1011
BibRef

Jin, X.B.[Xiao-Bo], Hou, X.W.[Xin-Wen], Liu, C.L.[Cheng-Lin],
Multi-class AdaBoost with Hypothesis Margin,
ICPR10(65-68).
IEEE DOI 1008
BibRef

Sternig, S.[Sabine], Godec, M.[Martin], Roth, P.M.[Peter M.], Bischof, H.[Horst],
TransientBoost: On-line boosting with transient data,
OLCV10(22-27).
IEEE DOI 1006
BibRef

Goto, Y.[Yuhi], Yamauchi, Y.[Yuji], Fujiyoshi, H.[Hironobu],
CS-HOG: Color similarity-based HOG,
FCV13(266-271).
IEEE DOI 1304
BibRef

Yamauchi, Y.[Yuji], Matsushima, C.[Chika], Yamashita, T.[Takayoshi], Fujiyoshi, H.[Hironobu],
Relational HOG feature with wild-card for object detection,
VS11(1785-1792).
IEEE DOI 1201
BibRef

Pelossof, R.[Raphael], Jones, M.[Michael], Vovsha, I.[Ilia], Rudin, C.[Cynthia],
Online coordinate boosting,
Learning09(1354-1361).
IEEE DOI 0910
BibRef

Babenko, B.[Boris], Yang, M.H.[Ming-Hsuan], Belongie, S.J.[Serge J.],
A family of online boosting algorithms,
Learning09(1346-1353).
IEEE DOI 0910
BibRef

Ji, Y.[Yi], Idrissi, K.[Khalid], Baskurt, A.[Atilla],
Object categorization using boosting within Hierarchical Bayesian model,
ICIP09(317-320).
IEEE DOI 0911
BibRef

Mahmood, A.[Arif], Khan, S.[Sohaib],
Early terminating algorithms for Adaboost based detectors,
ICIP09(1209-1212).
IEEE DOI 0911
BibRef

Venkataraman, V.[Vijay], Porikli, F.M.[Fatih M.],
RelCom: Relational combinatorics features for rapid object detection,
OTCBVS10(23-30).
IEEE DOI 1006
Simple features. BibRef

Hussein, M.[Mohamed], Porikli, F.M.[Fatih M.], Davis, L.S.[Larry S.],
Object detection via boosted deformable features,
ICIP09(1445-1448).
IEEE DOI 0911
BibRef

Allende-Cid, H.[Héctor], Mendoza, J.[Jorge], Allende, H.[Héctor], Canessa, E.[Enrique],
Semi-supervised Robust Alternating AdaBoost,
CIARP09(579-586).
Springer DOI 0911
BibRef

Zaidi, N.A.[Nayyar A.], Suter, D.[David],
Confidence rated boosting algorithm for generic object detection,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
Object Detection Using a Cascade of Classifiers,
DICTA08(600-605).
IEEE DOI 0812
BibRef

Jiang, Y.[Yan], Ding, X.Q.[Xiao-Qing],
Bhattacharyya boosting,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Seiffert, C.[Chris], Khoshgoftaar, T.M.[Taghi M.], van Hulse, J.[Jason], Napolitano, A.[Amri],
RUSBoost: Improving classification performance when training data is skewed,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Wang, S.J.[Shi-Jun], Zhang, C.S.[Chang-Shui],
Collaborative learning by boosting in distributed environments,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Wang, Z.J.[Zhan-Jun], Fang, C.[Chi], Ding, X.Q.[Xiao-Qing],
Asymmetric Real Adaboost,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Chouaib, H.[Hassan], Cloppet, F.[Florence], Vincent, N.[Nicole],
Fast Feature Selection for Handwritten Digit Recognition,
FHR12(485-490).
IEEE DOI 1302
BibRef

Chouaib, H.[Hassan], Vincent, N.[Nicole], Cloppet, F.[Florence], Tabbone, S.A.[Salvatore A.],
Generic Feature Selection and Document Processing,
ICDAR09(356-360).
IEEE DOI 0907
BibRef

Chouaib, H., Terrades, O.R.[O. Ramos], Tabbone, S.A., Cloppet, F., Vincent, N.,
Feature selection combining genetic algorithm and Adaboost classifiers,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Jiang, Y.[Yan], Ding, X.Q.[Xiao-Qing],
Partially Corrective AdaBoost,
SSPR08(469-478).
Springer DOI 0812
BibRef

Balntas, V.[Vassileios], Riba, E.[Edgar], Ponsa, D.[Daniel], Mikolajczyk, K.[Krystian],
Learning local feature descriptors with triplets and shallow convolutional neural networks,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Kalal, Z.[Zdenek], Matas, J.G.[Jiri G.], Mikolajczyk, K.[Krystian],
P-N learning: Bootstrapping binary classifiers by structural constraints,
CVPR10(49-56).
IEEE DOI 1006
BibRef
Earlier:
Online learning of robust object detectors during unstable tracking,
Learning09(1417-1424).
IEEE DOI 0910
BibRef
Earlier:
Weighted Sampling for Large-Scale Boosting,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Pham, M.T.[Minh-Tri], Hoang, V.D.D.[Viet-Dung D.], Cham, T.J.[Tat-Jen],
Detection with multi-exit asymmetric boosting,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Parag, T.[Toufiq], Elgammal, A.M.[Ahmed M.],
Higher Order Markov Networks for Model Estimation,
ISVC11(I: 246-258).
Springer DOI 1109
BibRef

Parag, T.[Toufiq], Elgammal, A.M.[Ahmed M.],
Supervised hypergraph labeling,
CVPR11(2289-2296).
IEEE DOI 1106
BibRef

Parag, T.[Toufiq], Elgammal, A.M.[Ahmed M.],
A voting approach to learn affinity matrix for robust clustering,
ICIP09(2409-2412).
IEEE DOI 0911
BibRef

Parag, T.[Toufiq], Porikli, F.M.[Fatih M.], Elgammal, A.M.[Ahmed M.],
Boosting adaptive linear weak classifiers for online learning and tracking,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Corso, J.J.[Jason J.],
Discriminative modeling by Boosting on Multilevel Aggregates,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Leistner, C.[Christian], Saffari, A.[Amir], Bischof, H.[Horst],
MIForests: Multiple-Instance Learning with Randomized Trees,
ECCV10(VI: 29-42).
Springer DOI 1009
BibRef

Zeisl, B.[Bernhard], Leistner, C.[Christian], Saffari, A.[Amir], Bischof, H.[Horst],
On-line semi-supervised multiple-instance boosting,
CVPR10(1879-1879).
IEEE DOI 1006

See also Learning Features for Tracking.
See also On-Line Multi-view Forests for Tracking.
See also On-line Hough Forests. BibRef

Leistner, C.[Christian], Saffari, A.[Amir], Roth, P.M.[Peter M.], Bischof, H.[Horst],
On robustness of on-line boosting: a competitive study,
Learning09(1362-1369).
IEEE DOI 0910
BibRef

Saffari, A.[Amir], Bischof, H.[Horst],
Boosting for Model-Based Data Clustering,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Allende-Cid, H.[Héctor], Salas, R.[Rodrigo], Allende, H.[Héctor], Ñanculef, R.[Ricardo],
Robust Alternating AdaBoost,
CIARP07(427-436).
Springer DOI 0711
BibRef

Jin, Y.X.[Yu-Xin], Tao, L.M.[Lin-Mi], Xu, G.Y.[Guang-You], Peng, Y.X.[Yu-Xin],
A Theoretical Approach to Construct Highly Discriminative Features with Application in AdaBoost,
ACCV07(I: 748-757).
Springer DOI 0711
BibRef

Vella, F.[Filippo], Lee, C.H.[Chin-Hui], Gaglio, S.[Salvatore],
Boosting of Maximal Figure of Merit Classifiers for Automatic Image Annotation,
ICIP07(II: 217-220).
IEEE DOI 0709
BibRef

Peng, S.W.[Shao-Wu], Lin, L.[Liang], Porway, J.[Jake], Sang, N.[Nong], Zhu, S.C.[Song-Chun],
Object Category Recognition Using Generative Template Boosting,
EMMCVPR07(198-212).
Springer DOI 0708
BibRef

Liu, W.[Wei], Chang, S.F.[Shih-Fu],
Robust multi-class transductive learning with graphs,
CVPR09(381-388).
IEEE DOI 0906
BibRef

Jiang, W.[Wei], Chang, S.F.[Shih-Fu], Jebara, T.[Tony], Loui, A.C.[Alexander C.],
Semantic Concept Classification by Joint Semi-supervised Learning of Feature Subspaces and Support Vector Machines,
ECCV08(IV: 270-283).
Springer DOI 0810

See also Video concept detection by audio-visual grouplets. BibRef

Jiang, W.[Wei], Zavesky, E.[Eric], Chang, S.F.[Shih-Fu], Loui, A.C.[Alex C.],
Cross-domain learning methods for high-level visual concept classification,
ICIP08(161-164).
IEEE DOI 0810
BibRef

Jiang, W.[Wei], Chang, S.F.[Shih-Fu], Loui, A.C.[Alexander C.],
Kernel Sharing With Joint Boosting For Multi-Class Concept Detection,
SLAM07(1-8).
IEEE DOI 0706
BibRef

Zhou, S.H.K.[Shao-Hua Kevin], Zhou, J.H.[Jing-Hao], Comaniciu, D.[Dorin],
A boosting regression approach to medical anatomy detection,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Pham, M.T.[Minh-Tri], Cham, T.J.[Tat-Jen],
Online Learning Asymmetric Boosted Classifiers for Object Detection,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Uray, M., Skocaj, D., Roth, P.M., Bischof, H., Leonardis, A.,
Incremental LDA Learning by Combining Reconstructive and Discriminative Approaches,
BMVC07(xx-yy).
PDF File. 0709

See also On-line boosting-based car detection from aerial images. BibRef

Renno, J.P.[John-Paul], Makris, D.[Dimitrios], Jones, G.A.[Graeme A.],
Object Classification in Visual Surveillance Using Adaboost,
VS07(1-8).
IEEE DOI 0706
BibRef

Kong, H.[Hui], Teoh, E.K.[Eam Khwang],
Coupling Adaboost and Random Subspace for Diversified Fisher Linear Discriminant,
ICARCV06(1-5).
IEEE DOI 0612
BibRef

Ghorayeb, H.[Hicham], Steux, B.[Bruno], Laurgeau, C.[Claude],
Boosted Algorithms for Visual Object Detection on Graphics Processing Units,
ACCV06(II:254-263).
Springer DOI 0601
BibRef

Etyngier, P.[Patrick], Paragios, N.[Nikos], Keriven, R.[Renaud], Genc, Y.[Yakup], Audibert, J.Y.[Jean-Yves],
Radon space and Adaboost for Pose Estimation,
ICPR06(I: 421-424).
IEEE DOI 0609
BibRef

Avidan, S.[Shai],
SpatialBoost: Adding Spatial Reasoning to AdaBoost,
ECCV06(IV: 386-396).
Springer DOI 0608
BibRef

Hao, W.[Wei], Luo, J.B.[Jie-Bo],
Generalized Multiclass AdaBoost and Its Applications to Multimedia Classification,
SLAM06(113).
IEEE DOI 0609
Extend AdaBoost from 2 classes to many.
See also Mining Compositional Features From GPS and Visual Cues for Event Recognition in Photo Collections. BibRef

Deng, W.H.[Wei-Hong], Hu, J.[Jiani], Guo, J.[Jun],
Ada-Boost Algorithm, Classification, Naïve-,
ICPR06(II: 699-702).
IEEE DOI 0609
Robust Fisher Linear Discriminant for dimensionality reduction BibRef

Li, W.L.[Wei-Liang], Gao, X.[Xiang], Zhu, Y.[Ying], Ramesh, V.[Visvanathan], Boult, T.E.[Terrance E.],
On the Small Sample Performance of Boosted Classifiers,
CVPR05(II: 574-581).
IEEE DOI 0507
BibRef

Lyu, S.W.[Si-Wei],
Infomax Boosting,
CVPR05(I: 533-538).
IEEE DOI 0507
BibRef

Bar-Hillel, A.[Aharon], Hertz, T.[Tomer], Weinshall, D.[Daphna],
Object Class Recognition by Boosting a Part-Based Model,
CVPR05(I: 702-709).
IEEE DOI 0507
BibRef

Huang, X.S.[Xiang-Sheng], Li, S.Z.[Stan Z.], Wang, Y.S.[Yang-Sheng],
Jensen-Shannon Boosting Learning for Object Recognition,
CVPR05(II: 144-149).
IEEE DOI 0507
BibRef

Wolf, L.B.[Lior B.], Martin, I.[Ian],
Robust Boosting for Learning from Few Examples,
CVPR05(I: 359-364).
IEEE DOI 0507
BibRef

Tu, Z.W.[Zhuo-Wen],
Learning Generative Models via Discriminative Approaches,
CVPR07(1-8).
IEEE DOI 0706
BibRef
Earlier:
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering,
ICCV05(II: 1589-1596).
IEEE DOI 0510
BibRef

Skarbek, W.[Wladyslaw], Kucharski, K.[Krzysztof],
Image Object Localization by AdaBoost Classifier,
ICIAR04(I: 511-518).
Springer DOI 0409
BibRef

Howe, N.R.[Nicholas R.], Ricketson, A.[Amanda],
Improving the Boosted Correlogram,
ICIAR04(I: 803-810).
Springer DOI 0409
BibRef

He, J.R.[Jing-Rui], Li, M.J.[Ming-Jing], Zhang, H.J.[Hong-Jiang], Zhang, C.S.[Chang-Shui],
W-boost and its application to web image classification,
ICPR04(I: 148-151).
IEEE DOI 0409
BibRef

Jiang, J.L., Loe, K.F.[Kia-Fock],
S-AdaBoost and Pattern Detection in Complex Environment,
CVPR03(I: 413-418).
IEEE DOI 0307
divide and conquer principle. Eliminate outliers. BibRef

Pavlovic, V.,
Model-based motion clustering using boosted mixture modeling,
CVPR04(I: 811-818).
IEEE DOI 0408
BibRef

Liu, C.[Ce], Shum, H.Y.[Hueng-Yeung],
Kullback-Leibler boosting,
CVPR03(I: 587-594).
IEEE DOI 0307
BibRef

Pavlov, D., Mao, J., Dom, B.,
Scaling-up Support Vector Machines Using Boosting Algorithm,
ICPR00(Vol II: 219-222).
IEEE DOI 0009
BibRef

Eibl, G., Pfeiffer, K.,
How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code,
Conference13th European Conference on Machine Learning, 2002, pp. 72-83. BibRef 0200

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Structural, Syntactic Methods for Image Analysis .