Multiple instance (MI) learning is a relatively new topic in machine learning. It is concerned with supervised learning but differs from normal supervised learning in two points: (1) it has multiple instances in an example (and there is only one instance in an example in standard supervised learning), and (2) only one class label is observable for all the instances in an example (whereas each instance has its own class label in normal supervised learning). In MI learning there is a common assumption regarding the relationship between the class label of an example and the “unobservable ” class labels of the instances inside it. This assumption, which is called the “MI assumption ” in this thesis, states that “An example is positive if at least one of its instances is positive and negative otherwise”. In this thesis, we first categorize current MI methods into a new framework. According to our analysis, there are two main categories of MI methods, instancebased and metadata-based approaches. Then we propose a new assumption for MI learning, called the “collective assumption”. Although this assumption has been used in some previous MI methods, it has never been explicitly stated, 1 and this is

In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithms were tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided. �DOI: 10.1115/1.3142727�