A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Interactive l earning using a \society o f m odels"
unpublished
Digital library access is driven by features, but features are often context-dependent a nd noisy, and their relevance for a query is not always obvious. This paper describes an approach f o r utilizing many d ata-dependent, user-dependent, and task-dependent features in a semi-automated tool. Instead of requiring universal similarity measures or manual selection of relevant f e a t ures, the approach p r o vides a learning algorithm for selecting and combining groupings of the data, where
fatcat:bsdd77qzfvhytas5myn6azcc74