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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)
Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or <i>query concept</i> by asking the user whether certain proposed images are relevant or not. For a relevance feedback(More)
We introduce the notion of restricted Bayes optimal classi-fiers. 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)
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)
With the emergence of multidrug-resistant bacterial strains, there is a dire need for new drug targets for antibacterial drug discovery and development. Filamentous temperature sensitive protein Z (FtsZ), is a GTP-dependent prokaryotic cell division protein, sharing less than 10% sequence identity with the eukaryotic cell division protein, tubulin. FtsZ(More)