다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
Knowledge-based systems, v.210, 2020년, pp.106447 -
Nikzad-Langerodi, Ramin (Software Competence Center Hagenberg GmbH (SCCH)) , Zellinger, Werner (Software Competence Center Hagenberg GmbH (SCCH)) , Saminger-Platz, Susanne (Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz (JKU)) , Moser, Bernhard A. (Software Competence Center Hagenberg GmbH (SCCH))
AI-Helper
Abstract We consider the problem of unsupervised domain adaptation (DA) in regression under the assumption of linear hypotheses (e.g. Beer–Lambert’s law) – a task recurrently encountered in analytical chemistry. Following the ideas from the non-linear iterative partial least squar...
IEEE Trans. Knowl. Data Eng. Pan 22 10 1345 2010 10.1109/TKDE.2009.191 A survey on transfer learning
Mach. Learn. Ben-David 79 1 151 2010 10.1007/s10994-009-5152-4 A theory of learning from different domains
J. Mach. Learn. Res. Ganin 17 Jan 1 2016 Domain-adversarial training of neural networks
Chemometr. Intell. Lab. Syst. Malli 161 49 2017 10.1016/j.chemolab.2016.12.008 Standard-free calibration transfer-An evaluation of different techniques
Anal. Chim. Acta Nikzad-Langerodi 1013 1 2018 10.1016/j.aca.2018.02.003 Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation
Appl. Spectrosc. Workman 72 3 340 2018 10.1177/0003702817736064 A review of calibration transfer practices and instrument differences in spectroscopy
J. Chemometr. Andries 31 4 e2818 2017 10.1002/cem.2818 Penalized eigendecompositions: motivations from domain adaptation for calibration transfer
Nikzad-Langerodi 2020 Graph-based calibration transfer
J. Chem. Educ. Swinehart 39 7 333 1962 10.1021/ed039p333 The Beer-Lambert law
Mark 2010 Chemometrics in Spectroscopy
J. Multivariate Anal. Bradley 11 1 1 1981 10.1016/0047-259X(81)90128-7 Central limit theorems under weak dependence
J. Appl. Probab. Wold 12 S1 117 1975 10.1017/S0021900200047604 Soft modelling by latent variables: the non-linear iterative partial least squares (NIPALS) approach
Nikzad-Langerodi 581 2019 2019 18th IEEE International Conference on Machine Learning and Applications Domain-invariant regression under Beer-Lambert’s law
J. Stat. Plann. Inference Shimodaira 90 2 227 2000 10.1016/S0378-3758(00)00115-4 Improving predictive inference under covariate shift by weighting the log-likelihood function
Huang 601 2007 Advances in Neural Information Processing Systems Correcting sample selection bias by unlabeled data
Gretton 2009 Covariate Shift by Kernel Mean Matching
Blitzer 120 2006 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing Domain adaptation with structural correspondence learning
IEEE Trans. Neural Netw. Pan 22 2 199 2011 10.1109/TNN.2010.2091281 Domain adaptation via transfer component analysis
J. Mach. Learn. Res. Gretton 13 3 723 2012 A kernel two-sample test
IEEE Trans. Pattern Anal. Mach. Intell. Ghifary 39 1414 2017 10.1109/TPAMI.2016.2599532 Scatter component analysis: A unified framework for domain adaptation and domain generalization
IEEE Access Sun 7 142551 2019 10.1109/ACCESS.2019.2944226 Informative feature selection for domain adaptation
IEEE Trans. Neural Netw. Learn. Syst. Deng 30 4 1180 2019 10.1109/TNNLS.2018.2863240 Domain adaption via feature selection on explicit feature map
Baktashmotlagh 769 2013 Proceedings of the 2013 IEEE International Conference on Computer Vision Unsupervised domain adaptation by domain invariant projection
Anal. Chem. Nikzad-Langerodi 90 11 6693 2018 10.1021/acs.analchem.8b00498 Domain-invariant partial-least-squares regression
J. Intell. Manuf. Zellinger 31 3 777 2020 10.1007/s10845-019-01499-4 Multi-source transfer learning of time series in cyclical manufacturing
Gong 2839 2016 International Conference on Machine Learning Domain adaptation with conditional transferable components
Courty 3733 2017 Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017 Joint distribution optimal transportation for domain adaptation
Gong 2066 2012 Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on Geodesic flow kernel for unsupervised domain adaptation
Sun 443 2016 Computer Vision-ECCV 2016 Workshops Deep coral: Correlation alignment for deep domain adaptation
IEEE Trans. Fuzzy Syst. Zuo 25 6 1795 2017 10.1109/TFUZZ.2016.2633376 Fuzzy regression transfer learning in Takagi-Sugeno fuzzy models
IEEE Trans. Fuzzy Syst. Zuo 26 2 847 2018 10.1109/TFUZZ.2017.2694801 Granular fuzzy regression domain adaptation in Takagi-Sugeno fuzzy models
Sugiyama 2012 Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Ben-David 137 2007 Advances in Neural Information Processing Systems Analysis of representations for domain adaptation
Dudley 2002 Real Analysis and Probability, Vol. 74
Zellinger 2020 Moment-Based Domain Adaptation: Learning Bounds and Algorithms
Ann. Statist. Barron 1347 1991 10.1214/aos/1176348252 Approximation of density functions by sequences of exponential families
Cover 2012 Elements of Information Theory
Int. Stat. Rev. Gibbs 70 3 419 2002 10.1111/j.1751-5823.2002.tb00178.x On choosing and bounding probability metrics
Anal. Chim. Acta Geladi 185 1 1986 10.1016/0003-2670(86)80028-9 Partial least-squares regression: a tutorial
Chemometr. Intell. Lab. Syst. Manne 2 1 187 1987 10.1016/0169-7439(87)80096-5 Analysis of two partial-least-squares algorithms for multivariate calibration
10.1145/301250.301389 V.Y. Pan, Z.Q. Chen, The complexity of the matrix eigenproblem, in: Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, 1999, pp. 507-516.
Sun 2016 AAAI Return of frustratingly easy domain adaptation
Hastie 2001 The Elements of Statistical Learning
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.