Topic
Multiple kernel learning
About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: Simulation results indicate that the proposed algorithm can not only solve themodel selection problem in SVM incremental learning, but also improve the classification or prediction precision.
10 citations
•
01 Jan 2016TL;DR: In this article, the authors present a new architecture of FERT, which is realized through results of a preliminary analysis across selected facial features descriptive techniques, namely, Gabor wavelet, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and further more, emotion recognition of the FERT scheme employs Multiple Kernel Learning (MKL) framework which reportedly outperforms traditional classifiers.
Abstract: A Vision of most e-learning models is to accurately recognize learner's post (pre) learning feedbacks to improve learning interaction. Several effort towards user centred e-learning have been made in literature, but mostly concentrates on cognitive based feedbacks for learner's modelling. However, Beside cognitive factors, emotions of the learner are equally important but seldom neglected. This paper present a new architecture of FERT. A processing pipeline of the FERT is realized through results of a preliminary analysis across selected facial features descriptive techniques, namely, Gabor wavelet, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Further more, Emotion recognition of the FERT scheme employs Multiple Kernel Learning (MKL) framework which reportedly outperforms traditional classifiers. Experiments have been conducted on contextual emotion datasets and results shows good performances of the FERT scheme. Finally, a conceptually user centred e-learning model that has potentials for improving learning interaction is described.
10 citations
••
TL;DR: Experimental evidences show that the proposed approach scales better than the SMO-MKL algorithm for tasks involving about several hundred thousands of examples, and comparisons with interior point methods prove the efficiency of the algorithm.
10 citations
••
TL;DR: A graph-based multimodal semi-supervised image classification (GraMSIC) framework to handle an image classification task where training images come along with tags, but only a subset being labeled, and the goal is to predict the class label of test images without tags is proposed.
10 citations
••
TL;DR: It is pointed out that when age estimation is treated as a multiple task learning (MTL) problem, the impact of training sample problem can be relieved and the proposed DLMTL model can be formulated into a very concise inner product representation.
10 citations