Results 1 - 10 of 1,736

Conditional random fields: Probabilistic models for segmenting and labeling sequence data

by John Lafferty , 2001
"... We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions ..."
Abstract - Cited by 3485 (85 self) - Add to MetaCart
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions

Algorithms for simultaneous sparse approximation. Part II: Convex relaxation

by Joel A. Tropp, Anna C. Gilbert, Martin, J. Strauss, J. A. Tropp, A. C. Gilbert, M. J. Strauss , 2004
"... Abstract. A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals th ..."
Abstract - Cited by 366 (5 self) - Add to MetaCart
approximation. Then it presents some numerical experiments that demonstrate how a sparse model for the input signals can be identified more reliably given several input signals. Afterward, the paper proves that the S-OMP algorithm can compute provably good solutions to several simultaneous sparse approximation

An Efficient Quasi-Maximum Likelihood Decoding for Finite Constellations

by Amin Mobasher, Mahmoud Taherzadeh, Renata Sotirov, Amir K. Khandani - in Conference on Information Sciences and Systems (CISS) 2005 , 2005
"... In Multi-Input Multi-Output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N- dimensional complex space. In general, this algorithm is shown to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on SemiDefinite ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. The general algorithm built on these models has a near-ML performance with polynomial computational complexity.

An Efficient Quasi-Maximum Likelihood Decoding for Finite Constellations

by Amir K. Khandani, Amin Mobasher, Amin Mobasher, Mahmoud Taherzadeh, Mahmoud Taherzadeh, Renata Sotirov, Renata Sotirov, Amir K - in Conference on Information Sciences and Systems (CISS) 2005 , 2005
"... In Multi-Input Multi-Output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this algorithm is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on Semi-Defini ..."
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-Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. The resulting algorithms built on these models have near-ML performances with polynomial computational complexities.

A Near Maximum Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming

by Amin Mobasher, Mahmoud Taherzadeh, Renata Sotirov, Amir K. Khandani , 2005
"... In Multi-Input Multi-Output (MIMO) systems, Maximum-Likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this problem is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on Semi-Definite Pr ..."
Abstract - Cited by 28 (4 self) - Add to MetaCart
-Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational

Generating textures on arbitrary surfaces using reaction-diffusion

by Greg Turk - Computer Graphics , 1991
"... This paper describes a biologically motivated method of texture synthesis called reaction-diffusion and demonstrates how these textures can be generated in a manner that directly matches the geometry of a given surface. Reaction-diffusion is a process in which two or more chemicals diffuse at unequa ..."
Abstract - Cited by 283 (5 self) - Add to MetaCart
of assigning texture coordinates to a complex surface. A mesh is generated by evenly distributing points over the model using relaxation and then determining which points are adjacent by constructing their Voronoi regions. Textures are rendered directly from the mesh by using a weighted sum of mesh values

Further relaxations of the SDP approach to sensor network localization

by Zizhuo Wang, Song Zheng, Stephen Boyd, Yinyu Ye - SIAM J. Optim
"... Recently, a semidefinite programming (SDP) relaxation approach has been proposed to solve the sensor network localization problem. Although it achieves high accuracy in esti-mating sensor’s locations, the speed of the SDP approach is not satisfactory for practical applications. In this paper we prop ..."
Abstract - Cited by 34 (0 self) - Add to MetaCart
SDP relaxation, tested to be both efficient and accurate in practical computations. The speed of the SSDP is much faster than that of the SDP approach as well as other approaches. We also prove several theoretical properties of the new SSDP relaxations.

Fast Approximation Algorithms for Fractional Packing and Covering Problems

by Serge A. Plotkin, David B. Shmoys, Éva Tardos , 1995
"... This paper presents fast algorithms that find approximate solutions for a general class of problems, which we call fractional packing and covering problems. The only previously known algorithms for solving these problems are based on general linear programming techniques. The techniques developed ..."
Abstract - Cited by 260 (13 self) - Add to MetaCart
theoretical analysis of the running time of a Lagrangean relaxation-based algorithm. We give several applications of our algorithms. The new approach yields several orders of magnitude of improvement over the best previously known running times for algorithms for the scheduling of unrelated parallel

SDP relaxations for some combinatorial optimization problems

by Renata Sotirov , 2010
"... In this chapter we present recent developments on solving various combinatorial optimization problems by using semidefinite programming (SDP). We present several SDP relaxations of the quadratic assignment problem and the traveling salesman problem. Further, we show the equivalence of several known ..."
Abstract - Cited by 5 (4 self) - Add to MetaCart
In this chapter we present recent developments on solving various combinatorial optimization problems by using semidefinite programming (SDP). We present several SDP relaxations of the quadratic assignment problem and the traveling salesman problem. Further, we show the equivalence of several known

Structural matching by discrete relaxation

by Richard C. Wilson, Edwin R. Hancock - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1997
"... Abstract—This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects ..."
Abstract - Cited by 143 (35 self) - Add to MetaCart
Abstract—This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main
Results 1 - 10 of 1,736