Domain Adaptation Guided Drift Compensation
暂无分享,去创建一个
David Zhang | Lei Zhang | Fengchun Tian | David Zhang | Lei Zhang | F. Tian
[1] Robert K. L. Gay,et al. Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.
[2] Ivor W. Tsang,et al. Domain adaptation from multiple sources via auxiliary classifiers , 2009, ICML '09.
[3] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[4] John Blitzer,et al. Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.
[5] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[6] Shuzhi Sam Ge,et al. Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption , 2014, IEEE Sensors Journal.
[7] Wei Jiang,et al. Fast sparse approximation of extreme learning machine , 2014, Neurocomputing.
[8] Qi Ye,et al. Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine , 2012 .
[9] Lei Zhang,et al. Performance Study of Multilayer Perceptrons in a Low-Cost Electronic Nose , 2014, IEEE Transactions on Instrumentation and Measurement.
[10] Lei Zhang,et al. A new kernel discriminant analysis framework for electronic nose recognition. , 2014, Analytica chimica acta.
[11] Lei Zhang,et al. Gases concentration estimation using heuristics and bio-inspired optimization models for experimental chemical electronic nose , 2011 .
[12] Fredrik Winquist,et al. Drift counteraction in odour recognition applications: lifelong calibration method , 1997 .
[13] M. Sjöström,et al. Drift correction for gas sensors using multivariate methods , 2000 .
[14] A. Kai Qin,et al. Evolutionary extreme learning machine , 2005, Pattern Recognit..
[15] Barun Das,et al. Towards Versatile Electronic Nose Pattern Classifier for Black Tea Quality Evaluation: An Incremental Fuzzy Approach , 2009, IEEE Transactions on Instrumentation and Measurement.
[16] Stanislaw Osowski,et al. Recognition of Coffee Using Differential Electronic Nose , 2012, IEEE Transactions on Instrumentation and Measurement.
[17] Danwei Wang,et al. Sparse Extreme Learning Machine for Classification , 2014, IEEE Transactions on Cybernetics.
[18] Rama Chellappa,et al. Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.
[19] Rong Yan,et al. Cross-domain video concept detection using adaptive svms , 2007, ACM Multimedia.
[20] J. Gardner,et al. An electronic nose system to diagnose illness , 2000 .
[21] Shankar Vembu,et al. Chemical gas sensor drift compensation using classifier ensembles , 2012 .
[22] Ricardo Gutierrez-Osuna,et al. Pattern analysis for machine olfaction: a review , 2002 .
[23] Xin Yin,et al. Chaotic time series prediction of E-nose sensor drift in embedded phase space , 2013 .
[24] Chee Kheong Siew,et al. Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.
[25] K. Thangavel,et al. ELM-Based Ensemble Classifier for Gas Sensor Array Drift Dataset , 2014 .
[26] Matteo Falasconi,et al. Drift Correction Methods for Gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges , 2012 .
[27] Anne-Claude Romain,et al. Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview , 2009 .
[28] Yiqiang Chen,et al. Weighted extreme learning machine for imbalance learning , 2013, Neurocomputing.
[29] Giovanni Squillero,et al. Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation , 2011, Pattern Recognit. Lett..
[30] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[31] Cheng Wu,et al. Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.
[32] Ivor W. Tsang,et al. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Domain Adaptation from Multiple Sources: A Domain- , 2022 .
[33] Alexander Vergara,et al. On the calibration of sensor arrays for pattern recognition using the minimal number of experiments , 2014 .
[34] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[35] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.