Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
A user-independent real-time emotion recognition system for software agents in domestic environments
Engineering Applications of Artificial Intelligence
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Knowledge transfer via multiple model local structure mapping
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning from multiple source domains via consensus regularization
Proceedings of the 17th ACM conference on Information and knowledge management
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cross domain distribution adaptation via kernel mapping
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relaxed Transfer of Different Classes via Spectral Partition
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Domain adaptation via transfer component analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A theory of learning from different domains
Machine Learning
Discriminative Learning Under Covariate Shift
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Frustratingly easy semi-supervised domain adaptation
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Rademacher penalties and structural risk minimization
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
We consider the characterization of muscle fatigue through a noninvasive sensing mechanism such as Surface ElectroMyoGraphy (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the large variation in these signals across different individuals makes the task of modeling and classification of SEMG signals challenging. Indeed, the variation in SEMG parameters from subject to subject creates differences in the data distribution. In this article, we propose two transfer learning frameworks based on the multisource domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed frameworks, the SEMG data of a subject represent a domain; data from multiple subjects in the training set form the multiple source domains and the test subject data form the target domain. SEMG signals are predominantly different in conditional probability distribution across subjects. The key feature of the first framework is a novel weighting scheme that addresses the conditional probability distribution differences across multiple domains (subjects) and the key feature of the second framework is a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data. The weights for minimizing the marginal probability differences are estimated independently, while the weights for minimizing conditional probability differences are computed simultaneously by exploiting the potential interaction among multiple sources. We also provide a theoretical analysis on the generalization performance of the proposed multisource domain adaptation formulation using the weighted Rademacher complexity measure. We have validated the proposed frameworks on Surface ElectroMyoGram signals collected from 8 people during a fatigue-causing repetitive gripping activity. Comprehensive experiments on the SEMG dataset demonstrate that the proposed method improves the classification accuracy by 20% to 30% over the cases without any domain adaptation method and by 13% to 30% over existing state-of-the-art domain adaptation methods.