Learning vs. Feature Extraction and Example Query vs. Feature Query

Warning: This engine is for demo only. It is not for public use. It may crash due to computing resource competition. If you would like to see the demo of this engine and find it crashes, please send an email to chen@cs.panam.edu, so he can reboot the engine for you.

Features is part of the research on building an intelligent one-for-one search engine. The most important character of Features is that it not only learns from the user's document relevance feedback, but also automatically extracts document features relevant to a search query and learns from the user's feature relevance feedback so that it is able to speed up its search process and to enhance its search performance. It is powered by two adaptive learning algorithms that work concurrently, one for feature extraction and learning, the other for document learning. With the help of those two learning algorithms, Features is able to perform learning, feature extraction, document query and feature in real-time.