An Extended Kernel for Generalized Multiple-Instance Learning

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly better or even with the old kernel in the other applications, showing that an SVM using this kernel does not overfit despite its richer representation.

Omtvedt Innovation Awards Presented to Chris Calkins, Steven Jones, Rodger Johnson, and Sheila Scheideler

It's a great pleasure to welcome you to this very first recognition ceremony for the Omtvedt Innovation Awards. We are present here to honor innovation strengths of the Institute of Agriculture and Natural Resources, and certainly the four faculty members receiving today's awards are greatly deserving of this recognition. Just hearing about their work is gratifying!