PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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160 BIBLIOGRAPHY [Shalev-Shwartz, 2007] Shalev-Shwartz, S. (2007). Online Learning: Theory, Algorithms, and Applications. PhD thesis, The Hebrew University of Jerusalem. [Sharp, 2008] Sharp, T. (2008). Implementing decision trees and forests on a gpu. In ECCV. [Shen et al., 2005] Shen, D., Zhang, J., Su, J., Zhou, G., and Tan, C.-L. (2005). A collaborative ability measurement for co-training. Springer. [Shi and Tomasi, 1994] Shi, J. and Tomasi, C. (1994). Good features to track. In CVPR. [Shotton et al., 2008] Shotton, J., Johnson, M., and Cipolla, R. (2008). Semantic texton forests for image catergorization and segmentation. In CVPR. [Sindhwani et al., 2006] Sindhwani, V., Keerthi, S. S., and Chapelle, O. (2006). Deterministic annealing for semi-supervised kernel machines. In ICML. [Singh et al., 2009] Singh, A., Nowak, R., and Zhu, X. (2009). Unlabeled data: Now it helps, now it doesn’t. In Advances in Neural Information Processing Systems. [Socher and Fei-Fei, 2010] Socher, R. and Fei-Fei, L. (2010). Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. In CVPR, San Francisco, CA. [Stikic and Schiele, 2009] Stikic, M. and Schiele, B. (2009). Activity recognition from sparsely labeled data using multi-instance learning. In Proceedings of the 4th International Symposium on Location and Context Awareness (LoCA’09), page 156–173, Tokyo, Japan. Springer, Springer. [Szeliski, 2010] Szeliski, R. (2010). Springer, 1 edition. Computer Vision: Algorithms and Applications. [Tang et al., 2007] Tang, F., Brennan, S., Zao, Q., and Tao, W. (2007). Co-tracking using semi-supervised support vector machines. In ICCV. [Tieu and Viola, 2000] Tieu, K. and Viola, P. (2000). Boosting image retrieval. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 228–235. [Torralba et al., 2008] Torralba, A., Fergus, R., and Weiss, Y. (2008). Small codes and large databases for recognition. In CVPR. [Utgoff et al., 1997] Utgoff, E., Bergman, N., and Clouse, J. (1997). Decision tree induction based on efficient tree restructuring. Machine Learning.
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PhD Thesis Semi-Supervised Ensemble
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Statutory Declaration I declare tha
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Most of all, I would like to thank
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learning. Finally, we hypothesize t
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sten Teil dieser Arbeit schlagen wi
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ii CONTENTS 3.6 Graph-based Methods
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iv CONTENTS 10 Conclusion 137 10.1
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vi LIST OF FIGURES 4.8 Performance
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viii LIST OF FIGURES 9.7 Comparison
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x LIST OF FIGURES
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xii LIST OF TABLES 8.2 Results and
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xiv LIST OF ALGORITHMS
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2 Chapter 1. Introduction Figure 1.
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4 Chapter 1. Introduction the liter
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6 Chapter 1. Introduction 1.1 Contr
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8 Chapter 1. Introduction
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10 Chapter 2. Preliminaries and Not
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26 Chapter 3. Overview of Semi-Supe
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44 Chapter 4. SemiBoost and Visual
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62 Chapter 5. On-line Semi-Supervis
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84 Chapter 6. Semi-Supervised Rando
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100 Chapter 7. On-line Semi-Supervi
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102 Chapter 7. On-line Semi-Supervi
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104 Chapter 8. Multiple Instance Le
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Page 176 and 177: 150 BIBLIOGRAPHY [Balcan et al., 20
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Page 184 and 185: 158 BIBLIOGRAPHY [Nigam et al., 200
Page 188 and 189: 162 BIBLIOGRAPHY [Xu et al., 2009]
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