Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, research started investigating kernels for structured data. This paper considers kernels for multi-instance problems - a class of concepts on individuals represented by sets. The main result of this paper is a kernel on multi-instance data that can be shown to separate positive and negative sets under natural assumptions. This kernel compares favorably with state of the art multi-instance learning algorithms in an empirical study. Finally, we give some concluding remarks and propose future work that might further improve the results.
|Translated title of the contribution||Multi-Instance Kernels|
|Title of host publication||Unknown|
|Editors||Claude Sammut, Achim Hoffmann|
|Pages||179 - 186|
|Number of pages||7|
|Publication status||Published - Jul 2002|