Multiple-instance learning (MIL) is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. In this paper a novel algorithm has been introduced for multiple-instance learning. This method was inspired by both diverse density (DD) and its expectation maximization version (EM-DD). It converts MIL problem to a single-instance setting. This improved method has better accuracy and time complexity than DD and EM-DD. We apply it to drug activity prediction and image retrieval. The experiments show it has competitive accuracy values compared with other previous approaches. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Han, F., Wang, D., & Liao, X. (2007). An improved multiple-instance learning algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4491 LNCS, pp. 1104–1109). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_129
Mendeley helps you to discover research relevant for your work.