Results 1  10
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1,561,046
A Separator Theorem for Planar Graphs
, 1977
"... Let G be any nvertex planar graph. We prove that the vertices of G can be partitioned into three sets A, B, C such that no edge joins a vertex in A with a vertex in B, neither A nor B contains more than 2n/3 vertices, and C contains no more than 2& & vertices. We exhibit an algorithm which ..."
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Cited by 465 (1 self)
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Let G be any nvertex planar graph. We prove that the vertices of G can be partitioned into three sets A, B, C such that no edge joins a vertex in A with a vertex in B, neither A nor B contains more than 2n/3 vertices, and C contains no more than 2& & vertices. We exhibit an algorithm which
Irrelevant Features and the Subset Selection Problem
 MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL
, 1994
"... We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small highaccuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features ..."
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Cited by 741 (26 self)
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We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small highaccuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features
UNet: A UserLevel Network Interface for Parallel and Distributed Computing
 In Fifteenth ACM Symposium on Operating System Principles
, 1995
"... The UNet communication architecture provides processes with a virtual view of a network interface to enable userlevel access to highspeed communication devices. The architecture, implemented on standard workstations using offtheshelf ATM communication hardware, removes the kernel from the communi ..."
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Cited by 596 (17 self)
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, as well as novel abstractions like Active Messages can be implemented efficiently. A UNet prototype on an 8node ATM cluster of standard workstations offers 65 microseconds roundtrip latency and 15 Mbytes/sec bandwidth. It achieves TCP performance at maximum network bandwidth and demonstrates
A Maximum Entropy approach to Natural Language Processing
 COMPUTATIONAL LINGUISTICS
, 1996
"... The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. In this paper we des ..."
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Cited by 1341 (5 self)
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describe a method for statistical modeling based on maximum entropy. We present a maximumlikelihood approach for automatically constructing maximum entropy models and describe how to implement this approach efficiently, using as examples several problems in natural language processing.
Capacity of Fading Channels with Channel Side Information
, 1997
"... We obtain the Shannon capacity of a fading channel with channel side information at the transmitter and receiver, and at the receiver alone. The optimal power adaptation in the former case is "waterpouring" in time, analogous to waterpouring in frequency for timeinvariant frequencysele ..."
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Cited by 579 (23 self)
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We obtain the Shannon capacity of a fading channel with channel side information at the transmitter and receiver, and at the receiver alone. The optimal power adaptation in the former case is "waterpouring" in time, analogous to waterpouring in frequency for timeinvariant frequency
Maximum entropy markov models for information extraction and segmentation
, 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
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Cited by 554 (18 self)
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, capitalization, formatting, partofspeech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We
Symmetry and Related Properties via the Maximum Principle
, 1979
"... We prove symmetry, and some related properties, of positive solutions of second order elliptic equations. Our methods employ various forms of the maximum principle, and a device of moving parallel planes to a critical position, and then showing that the solution is symmetric about the limiting plan ..."
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Cited by 539 (4 self)
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We prove symmetry, and some related properties, of positive solutions of second order elliptic equations. Our methods employ various forms of the maximum principle, and a device of moving parallel planes to a critical position, and then showing that the solution is symmetric about the limiting
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1231 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
A maximum likelihood approach to continuous speech recognition
 IEEE Trans. Pattern Anal. Machine Intell
, 1983
"... AbstractSpeech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the ..."
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Cited by 472 (9 self)
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AbstractSpeech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining
A Maximum Entropy Model for PartOfSpeech Tagging
, 1996
"... This paper presents a statistical model which trains from a corpus annotated with PartOfSpeech tags and assigns them to previously unseen text with stateoftheart accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" t ..."
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Cited by 577 (1 self)
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This paper presents a statistical model which trains from a corpus annotated with PartOfSpeech tags and assigns them to previously unseen text with stateoftheart accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "
Results 1  10
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1,561,046