14.3.1 Boosting, AdaBoost Technique

Rätsch, G.[Gunnar], Mika, S.[Sebastian], Schölkopf, B.[Bernhard], Müller, K.R.[Klaus-Robert],
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification,
PAMI(24), No. 9, September 2002, pp. 1184-1199.
IEEE Abstract. IEEE Top Reference. 0209Equivalence of SVM ( See also Support Vector Machines. ) and boosting-like algorithm ( See also Boosting Performance in Neural Networks. ). BibRef

Frossyniotis, D., Likas, A.C., Stafylopatis, A.,
A clustering method based on boosting,
PRL(25), No. 6, 19 April 2004, pp. 641-654.
WWW Version. 0405At 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 0711Support 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 Reference 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.
WWW Version. 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 Reference 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 Reference 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 Reference 0602 BibRef
Earlier: A1, A3, A2, A4:
Weak Hypotheses and Boosting for Generic Object Detection and Recognition,
ECCV04(Vol II: 71-84).
WWW Version. 0405Extract 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 Reference 0506 BibRef

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

Fussenegger, M.[Michael], Opelt, A.[Andreas], Pinz, A.[Axel],
Object localization/segmentation using generic shape priors,
ICPR06(IV: 41-44).
WWW Version. 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 Reference 0809 BibRef
Earlier:
Incremental learning of object detectors using a visual shape alphabet,
CVPR06(I: 3-10).
IEEE DOI Reference 0606 BibRef
And:
A Boundary-Fragment-Model for Object Detection,
ECCV06(II: 575-588).
Springer DOI Reference 0608 BibRef

Wang, X.[Xiao], Wang, H.[Han],
Classification by evolutionary ensembles,
PR(39), No. 4, April 2006, pp. 595-607.
WWW Version. 0604Multiple 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.
WWW Version. 0606Cost-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.
WWW Version. 0703AdaBoost; 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.
WWW Version. 0704 BibRef
Earlier: ICPR06(II: 602-605).
WWW Version. 0609Real AdaBoost; Information theory; Entropy; Minimum description length principle (MDLP); Variable discretization; Object detection BibRef

Sun, Y.M.[Yan-Min], Kamel, M.S.[Mohamed S.], Wong, A.K.C.[Andrew K.C.], Wang, Y.[Yang],
Cost-sensitive boosting for classification of imbalanced data,
PR(40), No. 12, December 2007, pp. 3358-3378.
WWW Version. 0709Classification; Class imbalance problem; AdaBoost; Cost-sensitive learning BibRef

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

Gao, J.[Jun], Xie, Z.[Zhao], Wu, X.D.[Xin-Dong],
Generic object recognition with regional statistical models and layer joint boosting,
PRL(28), No. 16, December 2007, pp. 2227-2237.
WWW Version. 0711Generic object recognition; Regional statistical models; Layer joint boosting; Sharing-code maps; ECOC matrix 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 Reference 0802 BibRef

Rodriguez, J.J.[Juan J.], Maudes, J.[Jesus],
Boosting recombined weak classifiers,
PRL(29), No. 8, 1 June 2008, pp. 1049-1059.
WWW Version. 0804Boosting; 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.
WWW Version. 0711Ensemble 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.
WWW Version. 0804Feature selection; AdaBoost; Object detection BibRef

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

Kalal, Z., Matas, J.G., Mikolajczyk, K.,
Weighted Sampling for Large-Scale Boosting,
BMVC08(xx-yy).
PDF Version. 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 Reference 0806 BibRef

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

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

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

Allende-Cid, H.[Héctor], Salas, R.[Rodrigo], Allende, H.[Héctor], Ñanculef, R.[Ricardo],
Robust Alternating AdaBoost,
CIARP07(427-436).
Springer DOI Reference 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 Reference 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 Reference 0709 BibRef

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

Peng, S.[Shaowu], 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 Reference 0708 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 Reference 0810 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 Reference 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 Reference 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 Reference 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 Reference 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 Version. 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 Reference 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 Reference 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 Reference 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).
WWW Version. 0609 BibRef

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

Yuan, J.S.[Jun-Song], Wu, Y.[Ying],
Context-aware clustering,
CVPR08(1-8).
IEEE DOI Reference 0806 BibRef

Yuan, J.S.[Jun-Song], Luo, J.B.[Jie-Bo], Wu, Y.[Ying],
Mining compositional features for boosting,
CVPR08(1-8).
IEEE DOI Reference 0806 BibRef

Hao, W.[Wei], Luo, J.B.[Jie-Bo],
Generalized Multiclass AdaBoost and Its Applications to Multimedia Classification,
SLAM06(113).
IEEE DOI Reference 0609Extend AdaBoost from 2 classes to many. BibRef

Deng, W.H.[Wei-Hong], Hu, J.[Jiani], Guo, J.[Jun],
Ada-Boost Algorithm, Classification, Naïve-,
ICPR06(II: 699-702).
WWW Version. 0609Robust Fisher Linear Discriminant for dimensionality reduction 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 Reference 0711 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).
WWW Version. 0609 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 Reference 0507 BibRef

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

Šochman, J.[Jan], Matas, J.[Jirí],
Learning a Fast Emulator of a Binary Decision Process,
ACCV07(II: 236-245).
Springer DOI Reference 0711 BibRef
Earlier:
WaldBoost: Learning for Time Constrained Sequential Detection,
CVPR05(II: 150-156).
IEEE DOI Reference 0507 BibRef
Earlier:
Inter-stage feature propagation in cascade building with adaboost,
ICPR04(I: 236-239).
IEEE DOI Reference 0409 BibRef

Zhang, W.[Wei], Yu, B.[Bing], Zelinsky, G.J.[Gregory J.], Samaras, D.[Dimitris],
Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features,
CVPR05(II: 323-330).
IEEE DOI Reference 0507 BibRef

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

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

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

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

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

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

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

Pavlov, D., Mao, J., Dom, B.,
Scaling-up Support Vector Machines Using Boosting Algorithm,
ICPR00(Vol II: 219-222).
IEEE DOI Reference
HTML Version. 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
Syntactic Methods for Image Analysis .