Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
We propose and analyze two strategies to learn over unordered pairs with kernels, and provide a common theoretical framework to compare them. The strategies are related to methods...
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
Machine learning for predicting user clicks in Webbased search offers automated explanation of user activity. We address click prediction in the Web search scenario by introducing...
Ding Zhou, Levent Bolelli, Jia Li, C. Lee Giles, H...
Background: Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach f...
Changhui Yan, Michael Terribilini, Feihong Wu, Rob...