Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A general regression technique for learning transductions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Neural Networks - Special issue: Emotion and brain
Modeling naturalistic affective states via facial and vocal expressions recognition
Proceedings of the 8th international conference on Multimodal interfaces
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Static vs. dynamic modeling of human nonverbal behavior from multiple cues and modalities
Proceedings of the 2009 international conference on Multimodal interfaces
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
Audio-Visual Classification and Fusion of Spontaneous Affective Data in Likelihood Space
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Particle filtering with factorized likelihoods for tracking facial features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Affective Computing
Multivariate relevance vector machines for tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Robust continuous prediction of human emotions using multiscale dynamic cues
Proceedings of the 14th ACM international conference on Multimodal interaction
Dimensional and continuous analysis of emotions for multimedia applications: a tutorial overview
Proceedings of the 20th ACM international conference on Multimedia
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Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spatial dependencies between the output vectors, as well as repeating output patterns and input-output associations, that can provide more robust and accurate predictors when modeled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine (OA-RVM) regression framework that augments the traditional RVM regression by being able to learn non-linear input and output dependencies. Instead of depending solely on the input patterns, OA-RVM models output covariances within a predefined temporal window, thus capturing past, current and future context. As a result, output patterns manifested in the training data are captured within a formal probabilistic framework, and subsequently used during inference. As a proof of concept, we target the highly challenging problem of dimensional and continuous prediction of emotions, and evaluate the proposed framework by focusing on the case of multiple nonverbal cues, namely facial expressions, shoulder movements and audio cues. We demonstrate the advantages of the proposed OA-RVM regression by performing subject-independent evaluation using the SAL database that constitutes naturalistic conversational interactions. The experimental results show that OA-RVM regression outperforms the traditional RVM and SVM regression approaches in terms of accuracy of the prediction (evaluated using the Root Mean Squared Error) and structure of the prediction (evaluated using the correlation coefficient), generating more accurate and robust prediction models.