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26,767
Monotone Complexity
, 1990
"... We give a general complexity classification scheme for monotone computation, including monotone spacebounded and Turing machine models not previously considered. We propose monotone complexity classes including mAC i , mNC i , mLOGCFL, mBWBP , mL, mNL, mP , mBPP and mNP . We define a simple ..."
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Cited by 2837 (11 self)
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simple notion of monotone reducibility and exhibit complete problems. This provides a framework for stating existing results and asking new questions. We show that mNL (monotone nondeterministic logspace) is not closed under complementation, in contrast to Immerman's and Szelepcs &apos
Undirected STConnectivity in LogSpace
, 2004
"... We present a deterministic, logspace algorithm that solves stconnectivity in undirected graphs. The previous bound on the space complexity of undirected stconnectivity was log 4/3 (ยท) obtained by Armoni, TaShma, Wigderson and Zhou [ATSWZ00]. As undirected stconnectivity is complete for the clas ..."
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Cited by 166 (3 self)
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for the class of problems solvable by symmetric, nondeterministic, logspace computations (the class SL), this algorithm implies that SL = L (where L is the class of problems solvable by deterministic logspace computations). Our algorithm also implies logspace constructible universaltraversal sequences
ReachFewL = ReachUL
, 2011
"... We show that two complexity classes introduced about two decades ago are equal. ReachUL is the class of problems decided by nondeterministic logspace machines which on every input have at most one computation path from the start configuration to any other configuration. ReachFewL, a natural general ..."
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Cited by 1 (0 self)
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We show that two complexity classes introduced about two decades ago are equal. ReachUL is the class of problems decided by nondeterministic logspace machines which on every input have at most one computation path from the start configuration to any other configuration. ReachFewL, a natural
DOI 10.1007/s0003701200508 computational complexity REACHFEWL = REACHUL
"... Abstract. We show that two complexity classes introduced about two decades ago are unconditionally equal. ReachUL is the class of problems decided by nondeterministic logspace machines which on every input have at most one computation path from the start configuration to any other configuration. Re ..."
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Abstract. We show that two complexity classes introduced about two decades ago are unconditionally equal. ReachUL is the class of problems decided by nondeterministic logspace machines which on every input have at most one computation path from the start configuration to any other configuration
Relativization of questions about log space computability
 Mathematical Systems Theory
, 1976
"... A notion of log space Turing reducibility is introduced. It is used to define rlative notions of log space, A, and nondeterministic log space, 2; A. These classes are compared with the classes and 2; which were originally defined by Baker, Gill, and Solovay [BGS]. It is shown that there exists a com ..."
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A notion of log space Turing reducibility is introduced. It is used to define rlative notions of log space, A, and nondeterministic log space, 2; A. These classes are compared with the classes and 2; which were originally defined by Baker, Gill, and Solovay [BGS]. It is shown that there exists a
Nondeterministic Space is Closed Under Complementation
, 1988
"... this paper we show that nondeterministic space s(n) is closed under complementation, for s(n) greater than or equal to log n. It immediately follows that the contextsensitive languages are closed under complementation, thus settling a question raised by Kuroda in 1964 [9]. See Hartmanis and Hunt [4 ..."
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Cited by 262 (14 self)
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this paper we show that nondeterministic space s(n) is closed under complementation, for s(n) greater than or equal to log n. It immediately follows that the contextsensitive languages are closed under complementation, thus settling a question raised by Kuroda in 1964 [9]. See Hartmanis and Hunt
ReVirt: Enabling Intrusion Analysis through VirtualMachine Logging and Replay
 In Proceedings of the 2002 Symposium on Operating Systems Design and Implementation (OSDI
, 2002
"... Rights to individual papers remain with the author or the author's employer. Permission is granted for noncommercial reproduction of the work for educational or research purposes. This copyright notice must be included in the reproduced paper. USENIX acknowledges all trademarks herein. Current ..."
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Cited by 457 (25 self)
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system loggers have two problems: they depend on the integrity of the operating system being logged, and they do not save sufficient information to replay and analyze attacks that include any nondeterministic events. ReVirt removes the dependency on the target operating system by moving it into a
A Guided Tour to Approximate String Matching
 ACM COMPUTING SURVEYS
, 1999
"... We survey the current techniques to cope with the problem of string matching allowing errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining t ..."
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Cited by 584 (38 self)
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We survey the current techniques to cope with the problem of string matching allowing errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices according to each case. We conclude with some future work directions and open problems.
The strength of weak learnability
 Machine Learning
, 1990
"... Abstract. This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with h ..."
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Cited by 861 (24 self)
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Abstract. This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high probability is able to output an hypothesis that is correct on all but an arbitrarily small fraction of the instances. The concept class is weakly learnable if the learner can produce an hypothesis that performs only slightly better than random guessing. In this paper, it is shown that these two notions of learnability are equivalent. A method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy. This construction may have practical applications as a tool for efficiently converting a mediocre learning algorithm into one that performs extremely well. In addition, the construction has some interesting theoretical consequences, including a set of general upper bounds on the complexity of any strong learning algorithm as a function of the allowed error e.
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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, planning under uncertainty, sensorbased planning, visibility, decisiontheoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.
Results 1  10
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