the requirements for a cross-domain transfer facility, describes the design of the fbuf mechanism that meets these requirements, and experimentally quantifies the impact of fbufs on network performance. 1 Introduction Optimizing operations that cross protection domain boundaries has received a great deal

the requirements for a cross-domain transfer facility, describes the design of the fbuf mechanism that meets these requirements, and experimentally quanti-fies the impact of fbufs on network performance. 1

A framework for skill acquisition is proposed that includes two major stages in the development of a cognitive skill: a declarative stage in which facts about the skill domain are interpreted and a procedural stage in which the domain knowledge is directly embodied in procedures for performing

soccer domain, based on experience from source tasks in the gridworld domain. We empirically show, through the use of three distinct transfer metrics, that Rule Transfer is effective across these domains. 1.

feature space. Based on the learned similarity measures, our algorithm interprets the data from the source domain as the data in the domain with different confidence levels, thus accomplishing the cross-domain knowledge transfer task. Our algorithm is evaluated on several real-world datasets

Analogical learning has long been seen as a powerful way of extending the reach of one‟s knowledge. We present the domain transfer via analogy (DTA) method for learning new domain theories via cross-domain analogy. Our model uses analogies between pairs of textbook example problems, or worked

Lightweight Remote Procedure Call (LRPC) is a communication facility designed and optimized for communication between protection domains on the same machine. In contemporary small-kernel operating systems, existing RPC systems incur an unnecessarily high cost when used for the type of communication

. Traditional text classification approaches are not able to cope well with learning across different domains. In this paper, we propose a novel cross-domain text classification algorithm which extends the traditional probabilistic latent semantic analysis (PLSA) algorithm to integrate labeled and unlabeled

) approach, to transfer cross-domain image knowledge for the new computer vision tasks, such as clas-sifications. An efficient iterative algorithm to solve the pro-posed optimization problem is introduced. We perform the proposed approach on two benchmark image data sets to simulate the real world cross-domain

of domain knowledge on the learning component. We then describe the performance and learning elements of the transfer task, define the form of that resulting knowledge, and introduce an evaluation methodology for the experiments. The reported results show the effect of cross-domain transfer within