Active learning differs from "learning from examples" in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples alone, giving better generalization for a fixed number of training examples. In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning called selective sampling and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful." We test our implementation, called an SGnetwork, on three domains and observe significant improvement in generalization.

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The computational complexity of the provability problem in systems of modal propositional logic is investigated. Every problem computable in polynomial space is log space reducible to the provability problem in any modal system between K and $4.

Abstract-We present the unequal loss protection (ULP) framework in which unequal amounts of forward error correction are applied to progressive data to provide graceful degradation of image quality as packet losses increase. We develop a simple algorithm that can find a good assignment within the ULP framework. We use the Set Partitioning in Hierarchical Trees coder in this work, but our algorithm can protect any progressive compression scheme. In addition, we promote the use of a PMF of expected channel conditions so that our system can work with almost any model or estimate of packet losses. We find that when optimizing for an exponential packet loss model with a mean loss rate of 20% and using a total rate of 0.2 bits per pixel on the Lenna image, good image quality can be obtained even when 40% of transmitted packets are lost.Index Terms-Joint source/channel coding, lossy image transmission, lossy packet networks, packet erasure channel, packet loss, priority encoding transmission, Reed-Solomon coding, unequal loss protection.