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Tractable reasoning and efficient query answering in description logics: The DL-Lite family

by Diego Calvanese, G. De Giacomo, Domenico Lembo, Maurizio Lenzerini, Riccardo Rosati - J. OF AUTOMATED REASONING , 2007
"... We propose a new family of Description Logics (DLs), called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts, and checking satisfiability of the whole knowledge base, b ..."
Abstract - Cited by 497 (123 self) - Add to MetaCart
, but also answering complex queries (in particular, unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base. We show that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in the size

Loopy belief propagation for approximate inference: An empirical study. In:

by Kevin P Murphy , Yair Weiss , Michael I Jordan - Proceedings of Uncertainty in AI, , 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract - Cited by 676 (15 self) - Add to MetaCart
Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon

Serverless Network File Systems

by Thomas E. Anderson, Michael D. Dahlin, Jeanna M. Neefe, David A. Patterson, Drew S. Roselli, Randolph Y. Wang - ACM TRANSACTIONS ON COMPUTER SYSTEMS , 1995
"... In this paper, we propose a new paradigm for network file system design, serverless network file systems. While traditional network file systems rely on a central server machine, a serverless system utilizes workstations cooperating as peers to provide all file system services. Any machine in the sy ..."
Abstract - Cited by 473 (28 self) - Add to MetaCart
in the system can store, cache, or control any block of data. Our approach uses this location independence, in combination with fast local area networks, to provide better performance and scalability than traditional file systems. Further, because any machine in the system can assume the responsibilities of a

An analysis of Bayesian classifiers

by Pat Langley, Wayne Iba, Kevin Thompson - IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE , 1992
"... In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability that t ..."
Abstract - Cited by 440 (17 self) - Add to MetaCart
In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability

Policy gradient methods for reinforcement learning with function approximation.

by Richard S Sutton , David Mcallester , Satinder Singh , Yishay Mansour - In NIPS, , 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
Abstract - Cited by 439 (20 self) - Add to MetaCart
represented by its own function approximator, independent of the value function, and is updated according to the gradient of expected reward with respect to the policy parameters. Williams's REINFORCE method and actor-critic methods are examples of this approach. Our main new result is to show

From Incremental Learning to Model Independent Instance Selection - A Support Vector Machine Approach

by Nadeem Ahmed Syed, Huan Liu, Kah Kay Sung , 1999
"... With large amounts of data being available to machine learning community, the need to design techniques that scale well is more critical than ever before. As some data may be collected over long periods, there is also a continuous need to incorporate the new data into the previously learned conce ..."
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concept. Incremental learning techniques can satisfy the need for both the scalability and incremental update. In this paper, we categorize the incremental techniques into two broad categories: block by block vs instance by instance. We suggest three criteria to evaluate the robustness and reliability

Tcp-like congestion control for layered multicast data transfer

by Lorenzo Vicisano, Jon Crowcroft, Luigi Rizzo , 1998
"... Abstract—We present a novel congestion control algorithm suitable for use with cumulative, layered data streams in the MBone. Our algorithm behaves similarly to TCP congestion control algorithms, and shares bandwidth fairly with other instances of the protocol and with TCP flows. It is entirely rece ..."
Abstract - Cited by 379 (12 self) - Add to MetaCart
Abstract—We present a novel congestion control algorithm suitable for use with cumulative, layered data streams in the MBone. Our algorithm behaves similarly to TCP congestion control algorithms, and shares bandwidth fairly with other instances of the protocol and with TCP flows. It is entirely

Unsupervised namedentity extraction from the web: An experimental study.

by Oren Etzioni , Michael Cafarella , Doug Downey , Ana-Maria Popescu , Tal Shaked , Stephen Soderland , Daniel S Weld , Alexander Yates - Artificial Intelligence, , 2005
"... Abstract The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL's novel architecture and ..."
Abstract - Cited by 372 (39 self) - Add to MetaCart
;chemist" and "biologist" are identified as sub-classes of "scientist"). List Extraction locates lists of class instances, learns a "wrapper" for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL's domain-independent methods, the methods also

Proofs of partial knowledge and simplified design of witness hiding protocols

by Ronald Cramer, Ivan Damgård, Berry Schoenmakers , 1994
"... Suppose we are given a proof of knowledge P in which a prover demonstrates that he knows a solution to a given problem instance. Suppose also that we have a secret sharing scheme S on n participants. Then under certain assumptions on P and S, we show how to transform P into a witness indistinguishab ..."
Abstract - Cited by 335 (14 self) - Add to MetaCart
are involved. If the instances are independently generated, this can lead to witness hiding protocols, even if P did not have this property. Our transformation produces a protocol with the same number of rounds as P and communication complexity n times that of P. Our results use no unproven complexity

The adaptive nature of human categorization

by John R. Anderson - Psychological Review , 1991
"... A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partiti ..."
Abstract - Cited by 344 (2 self) - Add to MetaCart
partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning
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