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Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training(More)
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper uses active learning to determine the experiments that are most informative towards uncovering the(More)
Relevance feedback is a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively learns a user’s desired output or query concept by asking the user whether certain proposed images are relevant or not. For a learning algorithm to be effective, it must(More)
We introduce the notion of restricted Bayes optimal classifiers. These classifiers attempt to combine the flexibility of the generative approach to classification with the high accuracy associated with discriminative learning. They first create a model of the joint distribution over class labels and features. Instead of choosing the decision boundary(More)
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(More)