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282
Estimation of probabilities from sparse data for the language model component of a speech recognizer
 IEEE Transactions on Acoustics, Speech and Signal Processing
, 1987
"... AbstractThe description of a novel type of rngram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods. Wh ..."
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Cited by 799 (2 self)
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AbstractThe description of a novel type of rngram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods
DESIGN, IMPLEMENTATION, AND EVALUATION OF THE CONSTRAINT LANGUAGE cc(FD)
 J. LOGIC PROGRAMMING 1994:19, 20:1679
, 1994
"... This paper describes the design, implementation, and applications of the constraint logic language cc(FD). cc(FD) is a declarative nondeterministic constraint logic language over finite domains based on the cc framework [33], an extension of the CLP scheme [21]. Its constraint solver includes (nonl ..."
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Cited by 187 (9 self)
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This paper describes the design, implementation, and applications of the constraint logic language cc(FD). cc(FD) is a declarative nondeterministic constraint logic language over finite domains based on the cc framework [33], an extension of the CLP scheme [21]. Its constraint solver includes (nonlinear
The Set of Primitive Recursive Functions 1
"... Summary. We follow [31] in defining the set of primitive recursive functions. The important helper notion is the homogeneous function from finite sequences of natural numbers into natural numbers where homogeneous means that all the sequences in the domain are of the same length. The set of all such ..."
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Summary. We follow [31] in defining the set of primitive recursive functions. The important helper notion is the homogeneous function from finite sequences of natural numbers into natural numbers where homogeneous means that all the sequences in the domain are of the same length. The set of all
Primitive recursion and the chain antichain principle
, 2012
"... Let the chain antichain principle (CAC) be the statement that each partial order on N possesses an infinite chain or an infinite antichain. Chong, Slaman and Yang recently proved using forcing over nonstandard models of arithmetic that CAC is Π11conservative over RCA0 + Π 0 1CP and so in partic ..."
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Cited by 5 (1 self)
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recursive realizers for such statements. Moreover, our proof is finitary in the sense of Hilbert’s program. CAC implies that every sequence of real numbers has a monotone subsequence. This BolzanoWeierstraß like principle is commonly used in proofs. Our result makes it possible to extract primitive
Recursive NonLinear Estimation of Discontinuous Flow Fields
 In Third European Conference on Computer Vision
, 1994
"... This paper defines a temporal continuity constraint that expresses assumptions about the evolution of 2D image velocity, or optical flow, over a sequence of images. Temporal continuity is exploited to develop an incremental minimization framework that extends the minimization of a nonconvex objecti ..."
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Cited by 51 (3 self)
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convex objective function over time. Within this framework this paper describes an incremental continuation method for recursive nonlinear estimation that robustly and adaptively recovers optical flow with motion discontinuities over an image sequence.
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
 In Proceedings of the 20th conference on Uncertainty in artificial intelligence (UAI'04
, 2004
"... We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (CausalState Splitting Reconst ..."
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Cited by 49 (3 self)
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We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (CausalState Splitting
The Gaussian mixture probability hypothesis density filter
 IEEE Trans. SP
, 2006
"... Abstract — A new recursive algorithm is proposed for jointly estimating the timevarying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The approach involves modelling the respecti ..."
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Cited by 142 (19 self)
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Abstract — A new recursive algorithm is proposed for jointly estimating the timevarying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The approach involves modelling
On primitive recursive algorithms and the greatest common divisor function
 Theor. Comput. Sci
, 2003
"... Abstract. We establish linear lower bounds for the complexity of nontrivial, primitive recursive algorithms from piecewise linear given functions. The main corollary is that logtime algorithms for the greatest common divisor from such givens (such as Stein’s) cannot be matched in efficiency by prim ..."
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Cited by 6 (2 self)
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Abstract. We establish linear lower bounds for the complexity of nontrivial, primitive recursive algorithms from piecewise linear given functions. The main corollary is that logtime algorithms for the greatest common divisor from such givens (such as Stein’s) cannot be matched in efficiency
Adaptive Filters
"... Introduction An adaptive filter is defined as a selfdesigning system that relies for its operation on a recursive algorithm, which makes it possible for the filter to perform satisfactorily in an environment where knowledge of the relevant statistics is not available. Adaptive filters are classif ..."
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Cited by 140 (2 self)
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Introduction An adaptive filter is defined as a selfdesigning system that relies for its operation on a recursive algorithm, which makes it possible for the filter to perform satisfactorily in an environment where knowledge of the relevant statistics is not available. Adaptive filters
Ackermannian and PrimitiveRecursive Bounds with Dickson’s Lemma
"... Dickson’s Lemma is a simple yet powerful tool widely used in decidability proofs, especially when dealing with counters or related data structures in algorithmics, verification and modelchecking, constraint solving, logic, etc. While Dickson’s Lemma is wellknown, most computer scientists are not ..."
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Cited by 6 (0 self)
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are not aware of the complexity upper bounds that are entailed by its use. This is mainly because, on this issue, the existing literature is not very accessible. We propose a new analysis of the length of bad sequences over (N k, ≤), improving on earlier results and providing upper bounds that are essentially
Results 1  10
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282