Deep Learning Publication Navigator - subtopic: kernel learning


Year TitleAuthor
2017   Nonlinear Deep Kernel Learning for Image  M Jiu, H Sahbi
2017   Deep multiple multilayer kernel learning in core vector machines  AL Afzal, S Asharaf¬†
2017   Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data  MS Ayhan, V Raghavan
2017   Deep kernel learning in core vector machines  AL Afzal, S Asharaf
2017   Ensemble Application of Convolutional Neural Networks and Multiple Kernel Learning for Multimodal Sentiment Analysis  S Poria, H Peng, A Hussain, N Howard, E Cambria
2017   Deep kernel learning method for SAR image target recognition  X Chen, X Peng, R Duan, J Li¬†
2017   Multiple Kernel Learning for Hyperspectral Image Classification: A Review  Y Gu, J Chanussot, X Jia, JA Benediktsson
2016   Overview of Deep Kernel Learning Based Techniques and Applications  XY Chen, XY Peng, JB Li, Y Peng
2016   End-to-End Kernel Learning with Supervised Convolutional Kernel Networks  J Mairal
2016   Laplacian deep kernel learning for image annotation  M Jiu, H Sahbi
2016   Stochastic Variational Deep Kernel Learning  AG Wilson, Z Hu, R Salakhutdinov, EP Xing
2016   Supplementary Material: Deep Kernel Learning  AG Wilson, Z Hu, R Salakhutdinov, EP Xing
2015   Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset  X Liu, J Shi, Q Zhang
2015   Deep multilayer multiple kernel learning  I Rebai, Y BenAyed, W Mahdi
2015   Multilingual Subjectivity Detection Using Deep Multiple Kernel Learning  I Chaturvedi, E Cambria, F Zhu, L Qiu, WK Ng
2015   Higher-level Feature Combination via Multiple Kernel Learning for Image Classification  W Luo, J Yang, W Xu, J Li, J Zhang
2015   Deep Kernel Learning  AG Wilson, Z Hu, R Salakhutdinov, EP Xing
2015   Multi-Channel EEG based Sleep Stage Classification with Joint Collaborative Representation and Multiple Kernel Learning  J Shi, X Liu, Y Li, Q Zhang, Y Li, S Yin
2014   Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning  JK Chen, Z Chen, Z Chi, H Fu