Adaptive Sensing and Fusion of Multi-Sensor Data and Historical Information

Context plays an important role when performing underwater classification, and in this report we examine context from two perspectives. First, the classification of items within a single task is placed within the context of distinct concurrent or previous classification tasks multiple distinct data collections. This is referred to as multi-task learning MTL, and is implemented here in a statistical manner, using a simplified form of the Dirichlet process. In addition, when performing many classification tasks one has simultaneous access to all unlabeled data that must be classified, and therefore there is an opportunity to place the classification of any one feature vector within the context of all unlabeled feature vectors this is referred to as semi-supervised learning. In this report we integrate MTL and semi-supervised learning into a single framework, thereby exploiting two forms of contextual information.