Search Results for author:
Found 4 papers, 0 papers with code
Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies
The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life.
Scalable Kernel Learning via the Discriminant Information
Kernel approximation methods create explicit, low-dimensional kernel feature maps to deal with the high computational and memory complexity of standard techniques.
Supervising Nyström Methods via Negative Margin Support Vector Selection
They approximate explicit, low-dimensional feature mappings for kernel functions from the pairwise comparisons with the training data.
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
