A Comparison of Multi-instance Learning Algorithms

Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms. Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms. This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains. Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems

Effects of precipitation conditions on the membrane morphology and permeation characteristics

[[abstract]]The permeability and permselectivity of asymmetric and particulate membranes towards glucose and proteins of various molecular sizes were studied. It was found that the skin layer of asymmetric membranes was permeable to glucose and insulin but effectively prevent the permeation of immunoglobulins. This result parallels our interest for the development of artificial pancreas. It was also found that skinless particulate membranes exhibited not only high permeation rates with respect to albumin and immunoglobulins but also good selectivity between these components. Thus, particulate membranes has the potential to be used in separating albumin from immunoglobulins for treating disorders related to immunoglobulin abnormalities.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[incitationindex]]E