Current approaches to knowledge discovery can be differentiated based on the discovered models using the following criteria: effectiveness, understandability (to a user or expert in the domain) and evolvability (ability to adapt over time to a changing environment). Most current approaches satisfy understandability or effectiveness, but not simultaneously, while tending to ignore knowledge evolution. Here we show how knowledge representation based upon Cartesian granule features and a corresponding induction algorithm can effectively address these knowledge discovery criteria (in this paper the discussion is limited to understandability and effectiveness) across a wide variety of problem domains including control, image understanding and medical diagnosis.
Knowledge Discovery using Cartesian Granule Features with Applications
| Jimi Shanahan, James Baldwin, Trevor Martin |
| The proceedings of the Intn'l conference of the North American Fuzzy Information Processing Society, NAFIPS 1999, New York, pp 228-232 |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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