| Paper ID | D5-S7-T3.3 |
| Paper Title |
Synthesizing New Expertise via Collaboration |
| Authors |
Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck, California Institute of Technology, United States |
| Session |
D5-S7-T3: Classification I |
| Chaired Session: |
Saturday, 17 July, 00:00 - 00:20 |
| Engagement Session: |
Saturday, 17 July, 00:20 - 00:40 |
| Abstract |
Consider a set of classes and an uncertain input. Suppose, we do not have access to data and only have knowledge of perfect pairwise classifiers between a few classes in the set. How can we use this to gain knowledge about unknown pairwise classifiers? In this paper, we define a framework to analyze this problem. In particular, we define a knowledge graph where classes denote vertices and edges that are present only between classes with known classifiers. We derive necessary conditions on the edge weights for a knowledge graph to be valid. Further, we show that these conditions are also sufficient if the graph is a cycle, which can yield unintuitive results even when given perfect classifiers. We provide an algorithm to obtain upper and lower bounds on the weights of unknown edges in a knowledge graph.
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