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713,057
A universal algorithm for sequential data compression
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1977
"... A universal algorithm for sequential data compression is presented. Its performance is investigated with respect to a nonprobabilistic model of constrained sources. The compression ratio achieved by the proposed universal code uniformly approaches the lower bounds on the compression ratios attainabl ..."
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Cited by 1501 (7 self)
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A universal algorithm for sequential data compression is presented. Its performance is investigated with respect to a nonprobabilistic model of constrained sources. The compression ratio achieved by the proposed universal code uniformly approaches the lower bounds on the compression ratios
Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics
 J. Geophys. Res
, 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
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Cited by 782 (22 self)
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. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter
Markovian Models for Sequential Data
, 1996
"... Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many machine learning applications, especially for speech recognition. Furthermore, in the last few years, many new and promising probabilistic models related to HMMs have been proposed. We firs ..."
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Cited by 117 (2 self)
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Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many machine learning applications, especially for speech recognition. Furthermore, in the last few years, many new and promising probabilistic models related to HMMs have been proposed. We
Mining Sequential Patterns
, 1995
"... We are given a large database of customer transactions, where each transaction consists of customerid, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empiri ..."
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Cited by 1534 (7 self)
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, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scaleup experiments show
GEOSTATISTICS AND SEQUENTIAL DATA ASSIMILATION
"... Abstract. We review possibilities of introducing geostatistical concepts into the sequential assimilation of data into numerical models. The reduced rank square root filter and the ensemble Kalman filter are presented from this perspective. Contributions of geostatistics are discussed showing that ..."
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Abstract. We review possibilities of introducing geostatistical concepts into the sequential assimilation of data into numerical models. The reduced rank square root filter and the ensemble Kalman filter are presented from this perspective. Contributions of geostatistics are discussed showing
Dryad: Distributed DataParallel Programs from Sequential Building Blocks
 In EuroSys
, 2007
"... Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
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Cited by 730 (27 self)
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Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set
On Optimal Segmentation of Sequential Data
 In: Neural Networks for Signal Processing XIII , IEEE, NJ
, 2003
"... We present an algorithm that eciently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a byproduct, we obtain a regularization parameter that can be used to compute such salient segmentations { also on ..."
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Cited by 4 (1 self)
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We present an algorithm that eciently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a byproduct, we obtain a regularization parameter that can be used to compute such salient segmentations { also
Sequential Data Algebra Primitives
, 1996
"... this paper is to develop a family of data type specifications and a particular method for writing such specifications based on the four valued logic of [BBR95]. The method is an informal one cast in a number of design rules for specifications of data algebras. The phrase data algebra is used rather ..."
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Cited by 6 (4 self)
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of modules below can be viewed as primitives for data algebra. Because all operations including the logical connectives are sequential in the sense that they inspect their arguments in some definite order this style of data algebra is called sequential data algebra. Evidently what we propose is only a choice
A Sequential Algorithm for Training Text Classifiers
, 1994
"... The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was ..."
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Cited by 626 (10 self)
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The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers
Mining Sequential Patterns: Generalizations and Performance Improvements
 Research Report RJ 9994, IBM Almaden Research
, 1995
"... Abstract. The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a user ..."
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Cited by 748 (5 self)
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speci ed minimum support, where the support of a pattern is the number of datasequences that contain the pattern. An example of a sequential pattern is \5 % of customers bought `Foundation' and `Ringworld ' in one transaction, followed by `Second Foundation ' in a later transaction
Results 1  10
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713,057