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IEEE Transactions on Image Processing, ISSN 1057-7149, 2020, Volume 29, pp. 199 - 213
Domain adaptation aims to deal with learning problems in which the labeled training data and unlabeled testing data are differently distributed. Maximum mean... 
Measurement | Dimensionality reduction | Adaptation models | Laplace equations | Training data | maximum mean discrepancy | Domain adaptation | Minimization | graph embedding | Kernel | KERNEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
IEEE Transactions on Industrial Electronics, ISSN 0278-0046, 07/2018, Volume 65, Issue 7, pp. 5872 - 5881
Journal Article
Statistics and Probability Letters, ISSN 0167-7152, 01/2020, Volume 156, p. 108596
This paper is concerned with a consistent estimator of the maximum mean discrepancy in the Hilbert space, which is always asymptotically normally distributed.... 
Hilbert space | Asymptotic normality | Maximum mean discrepancy | Kernel method | STATISTICS & PROBABILITY | U-STATISTICS
Journal Article
by Zhao, ZX and Peng, H and Zhang, XC and Zheng, Y and Chen, F and Fang, L and Li, JY
BMC MEDICAL GENOMICS, ISSN 1755-8794, 12/2019, Volume 12, Issue 1, pp. 183 - 10
Journal Article
IEEE Transactions on Medical Imaging, ISSN 0278-0062, 03/2016, Volume 35, Issue 3, pp. 778 - 790
Journal Article
Pattern Recognition, ISSN 0031-3203, 11/2012, Volume 45, Issue 11, pp. 3962 - 3984
Domain adaptation learning (DAL) is a novel and effective technique to address pattern classification problems where the prior information for training is... 
Support vector machine | Maximum scatter discrepancy | Domain adaptation learning | Maximum mean discrepancy | Pattern classification | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Training | Support vector machines | Kernels | Consistency | Minimization | Scatter | Optimization | Adaptation
Journal Article
IEEE/ACM Transactions on Computational Biology and Bioinformatics, ISSN 1545-5963, 4/2019, pp. 1 - 1
Cross-species or Cross-platform data classification is a challenging problem in the field of bioinformatics, which aims to classify data samples in one... 
Manifolds | Training | Adaptation models | Maximum mean discrepancy | Statistical distributions | Cross-species | Domain Adaptation | Biology | Cross-platform | Kernel | Bioinformatics
Journal Article
Machine Learning, ISSN 0885-6125, 2019, Volume 108, Issue 8, pp. 1561 - 1599
Journal Article
Signal Processing: Image Communication, ISSN 0923-5965, 05/2017, Volume 54, pp. 66 - 80
Sparse reconstruction based saliency detection methods are gaining popularity for object detection and content-based image recovery due to its simplicity and... 
Human visual attention | Laplacian graph regularized | Maximum mean discrepancy | Salient regions detection | Sparse reconstruction error | OBJECT DETECTION | VISUAL SALIENCY | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), ISSN 2329-9290, 12/2018, Volume 26, Issue 12, pp. 2412 - 2422
Like many machine learning tasks, the performance of speaker verification SV systems degrades when training and test data come from very different... 
Training | Adaptation models | i-vectors | Training data | Machine learning | maximum mean discrepancy | NIST | Robustness | domain adaptation | Covariance matrices | Speech processing | Speaker verification | ACOUSTICS | ENGINEERING, ELECTRICAL & ELECTRONIC | Domains | Adaptation
Journal Article
IEEE Intelligent Systems, ISSN 1541-1672, 2/2020, pp. 1 - 1
Domain Adaptation Learning is an effective method for leveraging knowledge from the source domain with labels to a target domain without any labels. However,... 
Credit Risk | Low Rank Representation | Maximum Mean Discrepancy | Domain Adaptation Learning
Journal Article
IEEE Transactions on Industrial Electronics, ISSN 0278-0046, 11/2019, pp. 1 - 1
Deep transfer-learning-based diagnosis models are promising to apply diagnosis knowledge across related machines, but from which the collected data follow... 
Domain adaptation | Intelligent fault diagnosis | Maximum mean discrepancy | Deep transfer learning
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 1/2020, pp. 1 - 1
Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different... 
Partial Domain adaptation | Maximum Mean Discrepancy | Fine-grained Visual Recognition | Deep Transfer Leaning
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2014, Volume 8862, pp. 898 - 904
Journal Article
IEEE Transactions on Circuits and Systems for Video Technology, ISSN 1051-8215, 2/2019, pp. 1 - 1
Existing cross-media retrieval methods are mainly based on the condition where training set covers all the categories in testing set, which lack extensibility... 
generative adversarial networks | Cross-media retrieval | zero-shot learning | maximum mean discrepancy
Journal Article
Information Sciences, ISSN 0020-0255, 03/2015, Volume 298, pp. 237 - 256
Regarded as global methods, Maximum Mean Discrepancy (MMD) based transfer learning frameworks only reflect the global distribution discrepancy and structural... 
Support vector machines | Local weighted mean | Maximum mean discrepancy | Multi-label classification | Projected maximum local weighted mean discrepancy | DOMAIN ADAPTATION | COMPUTER SCIENCE, INFORMATION SYSTEMS | MODEL | REGULARIZATION | Learning | Classifiers | Construction | Methodology | Data sets | Texts | Benchmarking | Adaptation
Journal Article
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