Bayesian networks provide a natural, concise knowledge representation method for building knowledge‐based systems under uncertainty. We consider domains representable by general but sparse networks and characterized ...
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This paper presents a congestion control scheme based on alternate path routing. In the scheme, if a node encounters congestion or loses its preferred neighbor on its primary path to a destination, it sends data packe...
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Implementation bias in a specification is an arbitrary constraint in the solution space. This paper describes the problem of bias and then presents a model of the specification and design processes describing individu...
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Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are limited and scheduling is tight. Therefore, one needs to be able to differentiate l...
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The authors model a virtual circuit in a computer network as a sequence of servers in tandem. They explicitly take into account cross traffic at the servers from other virtual circuits. The analysis of the model leads...
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ISBN:
(纸本)0780306023
The authors model a virtual circuit in a computer network as a sequence of servers in tandem. They explicitly take into account cross traffic at the servers from other virtual circuits. The analysis of the model leads them to propose a novel flow control scheme, which they term send-time control. A measure to evaluate the performance of flow control schemes, called the packet performance index, is proposed. They compare the send-time control scheme with the window-based flow control scheme of transmission control protocol (TCP) using simulation. The results obtained show that send-time control is superior to the window-based scheme.
We have shown how graphical languages such as CODE/ROPE and PPSE can be used to design SIMD or data parallel programs. The advantages of this approach are machine independence, design clarity, automated program analys...
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It is well known that, for fixed k, to find the k-th largest of n elements n+(k-1)log2n+Θ(1) comparisons are necessary and sufficient. But do the same bounds apply if we use a different type of query? We show that th...
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作者：
ALOIMONOS, YDURIC, ZComputer Vision Laboratory
Center for Automation Research Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park 20742-3411 MD United States
Passive navigation refers to the ability of an organism or a robot that moves in its environment to determine its own motion precisely on the basis of some perceptual input, for the purposes of kinetic stabilization. ...
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While procrastination is generally regarded as an undesirable quality, this paper points out a potential advantage to some uses of procrastination. Working with a formal model of machine learning we show that machines...
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ISBN:
(纸本)089791497X
While procrastination is generally regarded as an undesirable quality, this paper points out a potential advantage to some uses of procrastination. Working with a formal model of machine learning we show that machines that procrastinate about how many learning trials are needed are demonstrably more powerful than machines that do not procrastinate. The model used essentially allows the learning machine to speculate about how many trials it will need, and to then later revise (increase) its estimate. The more often revision are allowed, the more the machine is procrastinating. We show that the more the machine is allowed to procrastinate, the larger the class of learning phenomena (represented by sets of recursive functions) becomes. The learning machines we study all endeavor to learn recursive functions. The recursive functions are sufficiently rich so as to be able to encode any computable mapping from descriptions of stimuli to the descriptions of the associated responses.
Generalization is a learning problem that has received considerable attention. The generalization problem is to take a finite sample of some concept and produce an algorithm that can produce all other (perhaps infinit...
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ISBN:
(纸本)089791497X
Generalization is a learning problem that has received considerable attention. The generalization problem is to take a finite sample of some concept and produce an algorithm that can produce all other (perhaps infinitely many) samples of the same concept. Inductive inference is the study of this problem in a most general framework [1]. The classification problem is to take a finite sample of some concept and decide which type of concept the sample is from. The choice of type is usually finite. If the mechanism performing the classification is limiting, e.g., it makes more and more conjectures as to a classification as time goes on, then the process can also be considered as a type of learning. Roughly, we will say that some suitable mechanism has learned an appropriate classification if its sequence of conjectures stabilizes at some point. In this paper we formalize, at a suitable level of abstraction, the classification problem and rigorously compare it to the generalization problem. Despite some obvious similarities, the two notions are shown to be distinct. The new formalism of classification is investigated further.
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