SemiBoost: Boosting for Semi-Supervised Learning

Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K.; Liu, Yi

doi:10.1109/tpami.2008.235

Cited by 306 publications

(196 citation statements)

References 18 publications

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“…In recent years, semi-supervised learning methods have been widely studied [27][28][29][30][31]. One classical semi-supervised learning method is co-training [29] which utilizes multi-view features to retrain the classifiers to obtain better performance.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semisupervised dictionary learning method using label propagation (SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors simultaneously. Extensive experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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“…In this paper, we call the the recently proposed SemiBoost approach [7] for semi-supervised learning. Consider a dataset {f 1 , f 2 , · · · , f n } and the corresponding label {y 1 , y 2 , · · · , y n }.…”

Section: Semi-supervised Ensemble Trackingmentioning

confidence: 99%

“…To tackle this problem, [7] proposed an approach to optimize the upper bound of the objective function. The core is to estimate the confidence of unlabelled sample f i to be classified as positive as…”

Section: Semi-supervised Ensemble Trackingmentioning

confidence: 99%

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Semi-supervised ensemble tracking

Liu

Sun

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we propose a semi-supervised ensemble tracking approach under the framework of particle filter. The particle filter is used not only for object searching, but also for unlabelled sample generation. By adopting the semi-supervised learning technology, these unlabelled samples which are generated online are utilized to progressively modify the classifier and make the ensemble tracker to be more robust to environment changing. On the other hand, utilizing semisupervised learning technology can avoid the drifting phenomenons which are often encountered when using supervised learning. Finally, the performance of the proposed approach is evaluated using real visual tracking examples.

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“…SemiBoost combines supervised learning with semisupervised learning by utilizing both the labelled and unlabelled training data [18]. Its purpose is to reduce the generalization error when the labelled training data are insufficient [19].…”

Section: Introductionmentioning

confidence: 99%

Penalized AdaBoost: Improving the Generalization Error of Gentle AdaBoost through a Margin Distribution

Nagahashi

2015

IEICE Trans. Inf. & Syst.

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SUMMARYGentle AdaBoost is widely used in object detection and pattern recognition due to its efficiency and stability. To focus on instances with small margins, Gentle AdaBoost assigns larger weights to these instances during the training. However, misclassification of small-margin instances can still occur, which will cause the weights of these instances to become larger and larger. Eventually, several large-weight instances might dominate the whole data distribution, encouraging Gentle AdaBoost to choose weak hypotheses that fit only these instances in the late training phase. This phenomenon, known as "classifier distortion", degrades the generalization error and can easily lead to overfitting since the deviation of all selected weak hypotheses is increased by the late-selected ones. To solve this problem, we propose a new variant which we call "Penalized AdaBoost". In each iteration, our approach not only penalizes the misclassification of instances with small margins but also restrains the weight increase for instances with minimal margins. Our method performs better than Gentle AdaBoost because it avoids the "classifier distortion" effectively. Experiments show that our method achieves far lower generalization errors and a similar training speed compared with Gentle AdaBoost.

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SemiBoost: Boosting for Semi-Supervised Learning

Cited by 306 publications

References 18 publications

Semi-supervised dictionary learning with label propagation for image classification

Semi-supervised dictionary learning with label propagation for image classification

Semi-supervised ensemble tracking

Penalized AdaBoost: Improving the Generalization Error of Gentle AdaBoost through a Margin Distribution

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