We present MI-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a collection (or bag) of d-dimensional points where each dimension corresponds to a feature. A label is provided for the bag, but not for the individual points within the bag. MI-Winnow is different from existing multipleinstance learning algorithms in several key ways. First, MI-Winnow allows each image to be converted into a bag in multiple ways to create training (and test) data that varies in both the number of dimensions per point, and in the kind of features used. Second, instead of learning a concept defined by a single point-and-scaling hypothesis, MI-Winnow allows the underlying concept to be described by combining a set of separators learned by Winnow. For content-based image retrieval applications, such a generalized hypothesis is important since there may be different ways to recognize which images are of interest.