Richard E. Ladner

Improving Generalization with Active Learning

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 calledselective 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 anSG-network, on three domains and observe significant improvement in generalization.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 SG-network, on three domains and observe significant improvement in generalization.

Propositional dynamic logic of regular programs

Abstract We introduce a fundamental propositional logical system based on modal logic for describing correctness, termination and equivalence of programs. We define a formal syntax and semantics for the propositional dynamic logic of regular programs and give several consequences of the definition. Principal conclusions are that deciding satisfiability of length n formulas requires time d n /log n for some d > 1, and that satisfiability can be decided in nondeterministic time c n for some c . We provide applications of the decision procedure to regular expressions, Ianov schemes, and classical systems of modal logic.

On the Structure of Polynomial Time Reducibility

Two notions of polynomml time reduclbihty, denoted here by ~ T e and <.~P, were defined by Cook and Karp, respectively The abstract propertms of these two relatmns on the domain of computable sets are investigated. Both relations prove to be dense and to have minimal pairs. Further , there is a strictly ascending sequence with a minimal pair of upper bounds to the sequence. Our method of showing density ymlds the result that if P ~ NP then there are members of NP -P that are not polynomml complete

Unequal loss protection: graceful degradation of image quality over packet erasure channels through forward error correction

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.

The Computational Complexity of Provability in Systems of Modal Propositional Logic

The computational complexity of the provability problem in systems of modal propositional logic is investigated. Every problem computable in polynomial space is

A comparison of polynomial time reducibilities

\log

Freedom to roam: a study of mobile device adoption and accessibility for people with visual and motor disabilities

space reducible to the provability problem in any modal system between K and

Usable gestures for blind people: understanding preference and performance

S4

The influence of caches on the performance of sorting

. In particular, the provability problem in K, T, and

Relativization of questions about log space computability

S4