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Similarity learning for semi-supervised multi-class boosting

Q Y Wang, P C Yuen, G C Feng
2011 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
The proposed similarity measure is then applied to a semisupervised multi-class boosting (SSMB) algorithm.  ...  In semi-supervised classification boosting, a similarity measure is demanded in order to measure the distance between samples (both labeled and unlabeled).  ...  By using the proposed similarity measure, we demonstrate that the existing semi-boosted algorithm could satisfy all three semi-supervised learning assumptions.  ... 
doi:10.1109/icassp.2011.5946756 dblp:conf/icassp/WangYF11 fatcat:24lutpxgdzgbfgty2jrktklzz4

Regularized multi-class semi-supervised boosting

Amir Saffari, Christian Leistner, Horst Bischof
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
In this paper, we directly address the multi-class semi-supervised learning problem by an efficient boosting method.  ...  Many semi-supervised learning algorithms only deal with binary classification. Their extension to the multi-class problem is usually obtained by repeatedly solving a set of binary problems.  ...  Discussion So far we have presented a Regularized Multi-class Semi-supervised Boosting algorithm, which we name RMS-Boost.  ... 
doi:10.1109/cvpr.2009.5206715 dblp:conf/cvpr/SaffariLB09 fatcat:6l6pwznyanf4locu6he3vycgnu

Regularized multi-class semi-supervised boosting

A. Saffari, C. Leistner, H. Bischof
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
In this paper, we directly address the multi-class semi-supervised learning problem by an efficient boosting method.  ...  Many semi-supervised learning algorithms only deal with binary classification. Their extension to the multi-class problem is usually obtained by repeatedly solving a set of binary problems.  ...  Discussion So far we have presented a Regularized Multi-class Semi-supervised Boosting algorithm, which we name RMS-Boost.  ... 
doi:10.1109/cvprw.2009.5206715 fatcat:itmyu6go2jcxrnwu6isgllnjoq

Robust Multi-View Boosting with Priors [chapter]

Amir Saffari, Christian Leistner, Martin Godec, Horst Bischof
2010 Lecture Notes in Computer Science  
Experimentally, we show that the multi-class boosting algorithms achieves state-of-theart results in machine learning benchmarks.  ...  Since we target multi-class applications, we first introduce a multi-class boosting algorithm based on maximizing the mutli-class classification margin.  ...  Semi-Supervised Boosting with Robust Loss Functions We now focus on developing the multi-class semi-supervised boosting algorithms based on the concept of learning from priors [16, 17] .  ... 
doi:10.1007/978-3-642-15558-1_56 fatcat:e4c425ighvbg7blsey3cgayoae

An AdaBoost Algorithm for Multiclass Semi-supervised Learning

Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh
2012 2012 IEEE 12th International Conference on Data Mining  
An AdaBoost algorithm for multiclass semi-supervised learning Tanha, J.; van Someren, M.W.; Afsarmanesh, H.  ...  Experimental results on a number of UCI datasets show that the proposed algorithm performs better than the stateof-the-art boosting algorithms for multiclass semi-supervised learning.  ...  Recently, in [11] a new boosting method is used for semi-supervised multiclass learning which uses similarity between predictions and data.  ... 
doi:10.1109/icdm.2012.119 dblp:conf/icdm/TanhaSA12 fatcat:hrwoirfupngq3jzaagohauvisy

Boosted multi-class semi-supervised learning for human action recognition

Tianzhu Zhang, Si Liu, Changsheng Xu, Hanqing Lu
2011 Pattern Recognition  
Secondly, boosted co-EM is employed for the semi-supervised model construction.  ...  Firstly, we formulate the action recognition in a multi-class semi-supervised learning problem to deal with the insufficient labeled data and high computational expense.  ...  Boosted multi-class semi-supervised learning algorithm Based on the adaBoost.MH and co-EM, we propose a novel boosted multi-class semi-supervised learning algorithm.  ... 
doi:10.1016/j.patcog.2010.06.018 fatcat:rkdzarosdbeg7kqzqvjahajkpq

An Extension of Semi-supervised Boosting to Multi-valued Classification Problems

Yuta Sakai, Kazuki Yasui, Kenta Mikawa, Masayuki Goto
2021 Total Quality Science  
In a semi-supervised learning setting, if the distribution of labeled data is biased in each category set, it is difficult to estimate the correct labeling for unlabeled data.  ...  Therefore, semisupervised learning that uses not only labeled training data but also a large amount of unlabeled data for acquiring an accurate classifier has recently received attention.  ...  We would also like to thank Editage (www.editage.jp) for English language editing.  ... 
doi:10.17929/tqs.6.60 fatcat:bqyiyptkircp5mrnhngwyc7ape

Regularized Boost for Semi-supervised Ranking [chapter]

Zhigao Miao, Juan Wang, Aimin Zhou, Ke Tang
2015 Proceedings in Adaptation, Learning and Optimization  
Several boosting algorithms have been extended to semi-supervised learning with various strategies.  ...  Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data.  ...  Conclusions We have proposed a local smoothness regularizer for semi-supervising boosting learning and demonstrated its effectiveness on different types of data sets.  ... 
doi:10.1007/978-3-319-13359-1_49 fatcat:4mafb7jwnzcbtoz4an5hrytkdq

Unsupervised and Online Update of Boosted Temporal Models: The UAL2Boost

Pedro Canotilho Ribeiro, Plinio Moreno, Jose Santos-Victor
2010 2010 Ninth International Conference on Machine Learning and Applications  
recursively the current classifier, reducing the storage constraints, (ii) a probabilistic unsupervised update that eliminates the necessity of labeled data in order to adapt the classifier and (iii) a multi-class  ...  the learning algorithm to each scenario.  ...  In this paper we propose an application of multi-class semi-supervised learning, incorporating an automatic tunning of classifiers during the online phase.  ... 
doi:10.1109/icmla.2010.143 dblp:conf/icmla/RibeiroMS10 fatcat:3qpurehbfvhxzja35j6fvnanu4

Information theoretic regularization for semi-supervised boosting

Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labeled and unlabeled training  ...  boosting algorithms which use the labeled data alone and a state-of-the-art semisupervised boosting algorithm.  ...  The authors wish to thank researchers at the 711th HPW/ RHCP lab of the Wright Patterson Air Force Base for providing them the EEG human mental workload classification dataset.  ... 
doi:10.1145/1557019.1557129 dblp:conf/kdd/ZhengWLL09 fatcat:vfrnwoonyrhqhle57c3s36fjpm

Ensemble deep learning: A review [article]

M.A. Ganaie and Minghui Hu and A.K. Malik and M. Tanveer and P.N. Suganthan
2022 arXiv   pre-print
ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models.  ...  This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers.  ...  With the development of semi-supervised deep learning, ensemble methods started to be integrated with semi-supervised deep learning.  ... 
arXiv:2104.02395v2 fatcat:lq73jqso5vadvnqfnnmw4zul4q

Semi-Supervised Random Forests

Christian Leistner, Amir Saffari, Jakob Santner, Horst Bischof
2009 2009 IEEE 12th International Conference on Computer Vision  
From this intuition, we develop a novel multi-class margin definition for the unlabeled data, and an iterative deterministic annealing-style training algorithm maximizing both the multi-class margin of  ...  This work extends the usage of Random Forests to Semi-Supervised Learning (SSL) problems. We show that traditional decision trees are optimizing multiclass margin maximizing loss functions.  ...  Learning Similar to the traditional regularization-based semi-supervised learning methods, we also regularize the loss for the labeled samples with a loss over the unlabeled samples.  ... 
doi:10.1109/iccv.2009.5459198 dblp:conf/iccv/LeistnerSSB09 fatcat:rfziptxqbjcothokvnyp7vouwm

Constrained Semi-Supervised Learning Using Attributes and Comparative Attributes [chapter]

Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta
2012 Lecture Notes in Computer Science  
We consider the problem of semi-supervised bootstrap learning for scene categorization.  ...  The goal of this paper is to exploit these relationships and constrain the semi-supervised learning problem.  ...  Acknowledgments: The authors would like to thank Tom Mitchell, Martial Hebert, Varun Ramakrishna and Debadeepta Dey for many helpful discussions.  ... 
doi:10.1007/978-3-642-33712-3_27 fatcat:sba532lngzff5opk33zugijy4i

Multi-View Semi-Supervised Learning for Dialog Act Segmentation of Speech

U. Guz, S. Cuendet, D. Hakkani-Tur, G. Tur
2010 IEEE Transactions on Audio, Speech, and Language Processing  
This work investigates the application of multi-view semi-supervised learning algorithms on the sentence boundary classification problem by using lexical and prosodic information.  ...  We especially focus on two semi-supervised learning approaches, namely, self-training and co-training.  ...  Magimai Doss for many helpful discussions.  ... 
doi:10.1109/tasl.2009.2028371 fatcat:wpldl6le25g67e6ey4unc2a6ma

Exploiting unlabeled data in ensemble methods

Kristin P. Bennett, Ayhan Demiriz, Richard Maclin
2002 Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02  
Unlike alternative approaches, ASSEMBLE does not require a semi-supervised learning method for the base classifier.  ...  ASSEMBLE can be used in conjunction with any cost-sensitive classification algorithm for both two-class and multi-class problems.  ...  This choice of pseudo-class allows us to derive a class of boosting algorithms that can be applied in semi-supervised learning situations.  ... 
doi:10.1145/775047.775090 dblp:conf/kdd/BennettDM02 fatcat:iyncihmghnavplxwxdtp3ieeoi
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