Krasimir Tonchev, Nikolay Neshov, Agata Manolova, Teodora Sechkova
large scale kernel machines, kernel approximation with random features, mood estimation
For many years the kernel methods were the primary tool for machine learning and computer vision. With their bad scalability for large dataset and the development of deep learning methods their usability decreased. In this work we show that the recent development of kernel approximation with random features can be used in real world applications. We build a mood estimation algorithm by utilizing multiple kernel learning approximated by random features. The algorithm is tested on popular large scale dataset and compared with state of the art methods.
Cite this paper
Krasimir Tonchev, Nikolay Neshov, Agata Manolova, Teodora Sechkova. (2017) Applications of Large Scale Kernel Machines for Real World Human Mood Estimation. International Journal of Signal Processing, 2, 50-53