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Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization [article]

Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2021 arXiv   pre-print
To overcome these problems, we propose a novel generic framework for semi-supervised ordinal regression based on the empirical risk minimization principle that is applicable to optimizing all of the metrics  ...  In this paper, we consider its semi-supervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor.  ...  Supervised Ordinal Regression (SV): Supervised ordinal regression here is based on empirical risk minimization of the task surrogate risk, which is described in Pedregosa et al.  ... 
arXiv:1901.11351v3 fatcat:uhmpyssi7nbjhaltjz6vb356ta

Data-driven analysis using multiple self-report questionnaires to identify college students at high risk of depressive disorder

Bongjae Choi, Geumsook Shim, Bumseok Jeong, Sungho Jo
2020 Scientific Reports  
Based on this, we proposed two data-driven diagnostic methods; unsupervised and semi-supervised.  ...  In addition, we provided the interpretation of diagnosis and statistical analysis of information using local interpretable model-agnostic explanations and ordinal logistic regression.  ...  We showed that the cut-off points for ordinal logistic regression based on the semi-supervised approach showed lower performance, but this was similar to the semi-supervised approach.  ... 
doi:10.1038/s41598-020-64709-7 pmid:32398788 fatcat:zhugrh5sazdfpa257venkrlz4i

A new approach: semi-supervised ordinal classification

2020 Turkish Journal of Electrical Engineering and Computer Sciences  
The experiments show that the proposed method improves the classification accuracy of the model 13 compared to the existing semi-supervised method on ordinal data.  ...  However, semi-5 supervised learning for ordinal classification is yet to be explored.  ...  [19] 2019 Empirical risk minimization Semi-supervised ordinal re- gression with Gaussian kernel (SEMI-Kernel) MAE MSE MZE Various domains [20] 2011 Max-coupled learning Semi-supervised  ... 
doi:10.3906/elk-2008-148 fatcat:aci6txlof5avpfocloi7owu3fa

Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization

Wanli Shi, Bin Gu, Xiang Li, Heng Huang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Semi-supervised ordinal regression (S2OR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabeled.  ...  In this paper, we propose an unbiased objective function for S2OR AUC optimization based on ordinal binary decomposition approach.  ...  On the other hand, S 2 OR optimization problems need to handle two different types of data, i.e., unlabeled dataset and the datasets of class i, while standard DSG focuses on minimizing the empirical risk  ... 
doi:10.1609/aaai.v34i04.6029 fatcat:6btc4o373rcerkq4pkxatwjmfi

Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization [article]

Wanli Shi, Bin Gu, Xinag Li, Heng Huang
2019 arXiv   pre-print
Semi-supervised ordinal regression (S^2OR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabeled.  ...  In this paper, we propose an unbiased objective function for S^2OR AUC optimization based on ordinal binary decomposition approach.  ...  On the other hand, S 2 OR optimization problems need to handle two different types of data, i.e., unlabeled dataset and the datasets of class i, while standard DSG focuses on minimizing the empirical risk  ... 
arXiv:1912.11193v1 fatcat:vlqlib6y4jgbfktqwztnbbscty

An Improved High Risk Prediction in Health Examination Record Using Data Mining

Narmatha A, R. Abinaya Revathy, Mr. Radhakrishnan S.
2017 IJARCCE  
Identifying participants at risk based on their current and past HERs is important for early warning and preventive intervention. Risk means unwanted outcomes such as mortality and morbidity.  ...  The proposed system presents a Semi-supervised learning algorithm to handle a challenging multi-class classification problem with substantial unlabelled cases.  ...  SVMs are based on the structural risk minimization principle, closely related to regularization theory.  ... 
doi:10.17148/ijarcce.2017.63132 fatcat:bcskfwmuyvh6vkdyrexzqpa3ei

A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression [article]

Joel Jaskari, Jyri J. Kivinen
2018 arXiv   pre-print
We describe how to do supervised, unsupervised and semi-supervised learning, and nominal and ordinal classification, with the model.  ...  We develop a novel probabilistic generative model based on the variational autoencoder approach.  ...  There has been some previous work on semi-supervised ordinal regression.  ... 
arXiv:1812.07352v2 fatcat:yvk5gcnlyzadfl23sgmjxgmx2q

RECENT ADVANCES ON SUPPORT VECTOR MACHINES RESEARCH

Yingjie Tian, Yong Shi, Xiaohui Liu
2012 Technological and Economic Development of Economy  
Third, we explore other important issues such as lp-norm SVM for feature selection, LOOSVM based on minimizing LOO error bound, probabilistic outputs for SVM, and rule extraction from SVM.  ...  general optimization problems, such as integer programming, semi-infinite programming, bi-level programming and so on.  ...  machine, semi-supervised SVM, Universum SVM, robust SVM, knowledge based SVM, and multi-instance SVM for nonstandard problems, as well as p l -norm SVM for feature selection, LOOSVM based on minimizing  ... 
doi:10.3846/20294913.2012.661205 fatcat:vpno6pdnxjcalpsefmutch5qxq

Multiview Semi-supervised Learning for Ranking Multilingual Documents [chapter]

Nicolas Usunier, Massih-Reza Amini, Cyril Goutte
2011 Lecture Notes in Computer Science  
We show that our proposed algorithm achieves significant improvements over both semi-supervised multiview classification and semi-supervised single-view rankers on a large multilingual collection of Reuters  ...  We describe a semi-supervised multiview ranking algorithm that exploits a global agreement between viewspecific ranking functions on a set of unlabeled observations.  ...  based on the minimization of the disagreement.  ... 
doi:10.1007/978-3-642-23808-6_29 fatcat:xdmqogqynje2tjfsdvzrfj4p7m

Superset Learning Based on Generalized Loss Minimization [chapter]

Eyke Hüllermeier, Weiwei Cheng
2015 Lecture Notes in Computer Science  
This idea is realized by means of a generalized risk minimization approach, using an extended loss function that compares precise predictions with set-valued observations.  ...  In standard supervised learning, each training instance is associated with an outcome from a corresponding output space (e.g., a class label in classification or a real number in regression).  ...  In summary, our approach to superset learning is based on the minimization of the empirical risk with respect to this generalized loss function.  ... 
doi:10.1007/978-3-319-23525-7_16 fatcat:wul6brmiqrdmvf46x57tz2tt5e

Kernel logistic PLS: A tool for supervised nonlinear dimensionality reduction and binary classification

Arthur Tenenhaus, Alain Giron, Emmanuel Viennet, Michel Béra, Gilbert Saporta, Bernard Fertil
2007 Computational Statistics & Data Analysis  
The KL-PLS algorithm can be seen as a supervised dimensionality reduction (complexity control step) followed by a classification based on logistic regression.  ...  The principles of KL-PLS are based on both PLS latent variables construction and learning with kernels.  ...  In order to make the expected risk small, both the empirical risk and the VC-dimension should be minimized at the same time.  ... 
doi:10.1016/j.csda.2007.01.004 fatcat:rdsuuxyohvclfkrhlkstkndylu

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information [article]

Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
2019 arXiv   pre-print
In this paper, we consider a semi-supervised regression setting, where we obtain additional ordinal (or comparison) information for the unlabeled samples.  ...  We provide a precise quantification of the usefulness of these types of ordinal feedback in both nonparametric and linear regression, showing that in many cases it is possible to accurately estimate an  ...  Acknowledgements We thank Hariank Muthakana for his help on the age prediction experiments.  ... 
arXiv:1806.03286v2 fatcat:lqxz32drsbapnmvlzu4efzdscu

A Short Introduction to Learning to Rank

Hang LI
2011 IEICE transactions on information and systems  
In such cases, the learning problem becomes minimization of the (regularized) empirical risk function based on the surrogate loss. Note that we adopt a machine learning formulation here.  ...  The learning task then becomes the minimization of the empirical risk function, as in other learning tasks.  ... 
doi:10.1587/transinf.e94.d.1854 fatcat:cqsvelusurgrddxvdb5g545s3y

Supervised Learning with Similarity Functions [article]

Purushottam Kar, Prateek Jain
2012 arXiv   pre-print
We demonstrate the effectiveness of our model on three important super-vised learning problems: a) real-valued regression, b) ordinal regression and c) ranking where we show that our method guarantees  ...  Finally, we report results of our learning algorithms on regression and ordinal regression tasks using non-PSD similarity functions and demonstrate the effectiveness of our algorithms, especially that  ...  Appendix E Ordinal Regression In this section we give missing utility and admissibility proofs for the similarity-based learning model for ordinal regression.  ... 
arXiv:1210.5840v1 fatcat:3tibntrwfzac7nebgkxnwbqiju

Classification from Triplet Comparison Data [article]

Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2020 arXiv   pre-print
Since the proposed method is based on the empirical risk minimization framework, it inherently has the advantage that any surrogate loss function and any model, including neural networks, can be easily  ...  Furthermore, we theoretically establish an estimation error bound for the proposed empirical risk minimizer.  ...  We thank Ikko Yamane and Han Bao for fruitful discussions on this work.  ... 
arXiv:1907.10225v3 fatcat:5rr7yy5mrzevzme66umjv4rcem
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