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Multi-class classification in nonparametric active learning

Boris Ndjia Njike, Xavier Siebert
2022 International Conference on Artificial Intelligence and Statistics  
Several works have recently focused on nonparametric active learning, especially in the binary classification setting under Hölder smoothness assumptions on the regression function.  ...  In this paper, we extend these results to multiclass classification under a more general smoothness assumption, which takes into account a broader class of underlying distributions.  ...  Multi-class classification in nonparametric active learning Then, there exists an event A 4 , such that P (A 4 ) ≥ 1 − δ/8 and (49) holds on A 4 . Thus we can easily conclude the first part. 2.  ... 
dblp:conf/aistats/NjikeS22 fatcat:bnqhnuflereibk7vjz3xkpuv6i

Nonparametric Gesture Labeling from Multi-modal Data [chapter]

Ju Yong Chang
2015 Lecture Notes in Computer Science  
For that purpose, a generative probabilistic model is formalized and it is constructed by nonparametrically estimating multi-modal densities from a training dataset.  ...  In addition to the conventional skeletal joint based features, appearance information near the active hand in the RGB image is exploited to capture the detailed motion of fingers.  ...  Specifically, a multi-class classification problem should be solved to assign every frame in a video one of the multiple labels corresponding to known gesture cat-egories.  ... 
doi:10.1007/978-3-319-16178-5_35 fatcat:styqac454vaszhfp6uhxiiuvxy

Editorial

2015 Intelligent Data Analysis  
Kang and Cho in the next article of this issue discuss the drawbacks of multi-class SVM based classifications and propose a multi-class classification approach that is based on a one class SVM.  ...  Their experiments reported in the article show their approach outperforms other SVMbased multi-class classification methods.  ... 
doi:10.3233/ida-150739 fatcat:sinsfishqbcnzcmijxs6hbz2eq

A General Common Spatial Patterns for EEG Analysis with Applications to Vigilance Detection

Hongbin Yu, Hongtao Lu, Shuihua Wang, Kaijian Xia, Yizhang Jiang, Pengjiang Qian
2019 IEEE Access  
and we then develop a new efficient algorithm based on matrix deflation to solve the proposed NCSP algorithm and its extensions-nonparametric multi-class CSP (NMCSP).  ...  In this paper, we extend the traditional CSP to the general version and proposed nonparametric CSP (NCSP) algorithms which do not explicitly rely on the assumption of the underlying class Gaussian distribution  ...  multi-class CSP to nonparametric multi-class CSP (NMCSP).  ... 
doi:10.1109/access.2019.2934519 fatcat:mtuos6vodrgk5j56q3ir4kl7ue

Multi-instance Metric Learning

Ye Xu, Wei Ping, Andrew T. Campbell
2011 2011 IEEE 11th International Conference on Data Mining  
To exploit the mechanism of how instances determine bag labels in multi-instance learning, we design a nonparametric density-estimation-based weighting scheme to assign higher "weights" to the instances  ...  In this paper, we propose a framework called Multi-Instance MEtric Learning (MIMEL) to learn an appropriate distance under the multi-instance setting.  ...  INTRODUCTION Multi-instance learning originated from the drug activity prediction problem [11] . In multi-instance learning, training samples are bags that contain many instances.  ... 
doi:10.1109/icdm.2011.106 dblp:conf/icdm/XuPC11 fatcat:o3zdxggjizdbbhqbfctzqllj24

Nonparametric Bayesian Method for Robot Anomaly Diagnose [chapter]

Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
2020 Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection  
In this chapter, we introduce two novel anomaly diagnose methods using the Bayesian nonparametric hidden Markov models when anomaly triggered, including i)multi-class classifier based on nonparametric  ...  Zhou et al., Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, https://doi.  ...  Then, the extracted features importance analysis using the hypothesis testing method, after that the filtered features are verified on 9 common out-of-the-box supervised learning methods for multi-class  ... 
doi:10.1007/978-981-15-6263-1_5 fatcat:gfvoox3mifhc3dutbcxastriu4

Author Index

2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
LPBoost Workshop: Context-driven Clustering by Multi-class Classification in an Active Learning Framework Workshop: TransientBoost: On-line Boosting with Transient Data Goferman, Stas Context-Aware  ...  Kernel Learning Safety in Numbers: Learning Categories from Few Examples with Multi Model Knowledge Transfer Workshop: OM-2: An Online Multi-class Multi-kernel Learning Algorithm Oreifej, Omar  ... 
doi:10.1109/cvpr.2010.5539913 fatcat:y6m5knstrzfyfin6jzusc42p54

Infinite Latent SVM for Classification and Multi-task Learning

Jun Zhu, Ning Chen, Eric P. Xing
2011 Neural Information Processing Systems  
with a nonparametric Bayesian model for discovering predictive latent features for classification and multi-task learning, respectively.  ...  Our results appear to demonstrate the merits inherited from both large-margin learning and Bayesian nonparametrics.  ...  Acknowledgments This work was done when JZ was a post-doc fellow in CMU.  ... 
dblp:conf/nips/ZhuCX11 fatcat:5tahalgw4bgr7by4c3g4bgbsm4

Stream-based joint exploration-exploitation active learning

C. C. Loy, T. M. Hospedales, Tao Xiang, Shaogang Gong
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
However, existing active learning methods either rely on heuristic multi-criteria weighting or are limited to batch processing.  ...  In this paper, we present a new unified framework for joint exploration-exploitation active learning in streams without any heuristic weighting.  ...  instances. • multi-kldiv -a multi-criteria active learning method [13] that combines qbc and lowlik for joint exploration-exploitation.  ... 
doi:10.1109/cvpr.2012.6247847 dblp:conf/cvpr/LoyHXG12 fatcat:ious32bsmnba7nltzs7gsul4nu

Factorial Multi-Task Learning : A Bayesian Nonparametric Approach

Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh
2013 International Conference on Machine Learning  
We apply our model for multi-task regression and classification applications.  ...  Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning.  ...  Further, all our comparisons are based on the RMSE for regression and multi-class classification error for classification.  ... 
dblp:conf/icml/GuptaPV13 fatcat:2k26sq3rnjdjra26q24yjsqwju

Automatic Classification of Academic and Vocational Guidance Questions using Multiclass Neural Network

Omar Zahour, El Habib, Ahmed Eddaoui, Oumaima Hourrane
2019 International Journal of Advanced Computer Science and Applications  
"E-Orientation Data" is a machine learning method based on John L. Holland's Theory of RIASEC typology that uses a multiclass neural network algorithm.  ...  This article deals with the problematic of educational and vocational orientation and we have developed a model for automatic classification of orientation questions.  ...  Categories: we have four classes (labels) of categories namely: 1) Activity 2) Occupations 3) Abilities 4) Personality In our research work on Guidance Classification, we used the Azure Machine Learning  ... 
doi:10.14569/ijacsa.2019.0101072 fatcat:6eha7zjvzzbwnidypbw5lufvvi

Multi-view face and eye detection using discriminant features

Peng Wang, Qiang Ji
2007 Computer Vision and Image Understanding  
Multi-view face detection plays an important role in many applications. This paper presents a statistical learning method to extract features and construct classifiers for multi-view face detection.  ...  Histograms of extracted features are learned to represent class distributions and to construct probabilistic classifiers.  ...  In Section 3, we present a recursive nonparametric discriminant analysis (RNDA) method to extract features for multi-view face and eye detection.  ... 
doi:10.1016/j.cviu.2006.08.008 fatcat:7r4d47mxerfldlthumfb4eaeeu

Analysis of Classification and Clustering based Novel Class Detection Techniques for Stream Data Mining

Kamini Tandel, Jignasa N. Patel
2015 International Journal of Engineering Research and  
Different classification and clustering based techniques are used to detect novel class in data stream.  ...  Classification is the challenging task in data stream and existing data mining classifier cannot detect novel class until the classification models are not trained.  ...  ActMiner: ActMiner, define as Active Classifier for Data Streams with Novel Class Miner that performs classification and novel class detection in data streams while requiring small amount of labeled data  ... 
doi:10.17577/ijertv4is100160 fatcat:cqtpjn4qxrb4pndtteg4uhxlfu

Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting

Daniel Zeiberg, Shantanu Jain, Predrag Radivojac
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Estimating class proportions has emerged as an important direction in positive-unlabeled learning.  ...  Motivated by this need, we propose an intuitive and fast nonparametric algorithm to estimate class proportions.  ...  Nonparametric estimation of mixing proportions in the PU setting has been actively researched.  ... 
doi:10.1609/aaai.v34i04.6151 fatcat:ckk4svif3zalhl24hpajrv376e

Bayesian multi-subject common spatial patterns with Indian Buffet process priors

Hyohyeong Kang, Seungjin Choi
2013 2013 IEEE International Conference on Acoustics, Speech and Signal Processing  
In the case of multi-subject EEG classification where brain waves recorded from multiple subjects who undergo the same mental task are available, it is desirable to capture intersubject relatedness in  ...  In this paper we present a nonparametric Bayesian model for a multi-subject extension of CSP where subject relatedness is captured by assuming that spatial patterns across subjects share a latent subspace  ...  In this paper we present a nonparametric Bayesian model for a multi-subject extension of CSP where given multi-subject EEG data, allowing inter-subject relatedness to be captured by assuming that spatial  ... 
doi:10.1109/icassp.2013.6638278 dblp:conf/icassp/KangC13 fatcat:42qrvcsayfczhlyb4mtmcfgsbm
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