| In many machine learning and statistical tasks, gathering data is time-consuming and costly; thus, finding ways to minimize the number of data instances is beneficial. In many cases, active learning can be employed. Here, we are permitted to actively choose future training data based upon the data that we have previously seen. When we are given this extra flexibility, we demonstrate that we can often reduce the need for large quantities of data. We explore active learning for three central areas of machine learning: classification, parameter estimation and causal discovery.; Support vector machine classifiers have met with significant success in numerous real-world classification tasks. However, they are typically used with a randomly selected training set. We present theoretical motivation and an algorithm for performing active learning with support vector machines. We apply our algorithm to text categorization and image retrieval and show that our method can significantly reduce the need for training data.; In the field of artificial intelligence, Bayesian networks have become the framework of choice for modeling uncertainty. Their parameters are often learned from data, which can be expensive to collect. The standard approach is to data that is randomly sampled from the underlying distribution. We show that the alternative approach of actively targeting data instances to collect is, in many cases, considerably better.; Our final direction is the fundamental scientific task of causal structure discovery from empirical data. Experimental data is crucial for accomplishing this task. Such data is often expensive and must be chosen with great care. We use active learning to determine the experiments to perform. We formalize the causal learning task as that of learning the structure of a causal Bayesian network and show that active learning can substantially reduce the number of experiments required to determine the underlying causal structure of a domain. |
