Learning to Visualize Data and Interact
We are developing semi-supervised and unsupervised machine learning algorithms that collaborate with users to develop visualizations that the users can use. This work is based on the techniques from the following areas.
- Kernel Methods
- Manifold Learning
- Active Learning
- Several areas of Nonlinear Dimensionality Reduction
- Prabhakar, S. Learning to Support another Learner. In Proceedings of Eighteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2005) (Clearwater Beach, Florida, May 2005) 844 - 845.
- Prabhakar, S. Autonomous Agents as Adaptive Perceptual Interfaces. In Papers from the 2005 AAAI Spring Symposium on Persistent Assistants: Living and Working with AI (Stanford Univ., CA. March 2005) 76 - 83.
- *Prabhakar, S. Learning to Visualize and Interact. In Preparation to be submitted to Pattern Recognition. Expected Submission Date: March 2011.
- *Prabhakar, S. Adaptive Manifolds. In Preparation to be submitted to ECML PKDD 2011. Athens Greece, September 2011.
- *Prabhakar, S. Kernel based feature formulation for visualization. In preparation to be submitted to Neural Information Processing Systems (NIPS) 2011,Granada, Spain, Dec 2011.