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LeastSquares Policy Iteration
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach ..."
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Cited by 461 (12 self)
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We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 446 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set
Posterior CramérRao bounds for discretetime nonlinear filtering
 IEEE Trans. Signal Processing
, 1998
"... Abstract—A meansquare error lower bound for the discretetime nonlinear filtering problem is derived based on the Van Trees (posterior) version of the Cramér–Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly nonGaussian, dynamical systems and is more general tha ..."
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Cited by 178 (4 self)
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Abstract—A meansquare error lower bound for the discretetime nonlinear filtering problem is derived based on the Van Trees (posterior) version of the Cramér–Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly nonGaussian, dynamical systems and is more general
Cisoid parameter estimation in the colored noise case: Asymptotic CramérRao bound, maximum likelihood and nonlinear leastsquares
, 1996
"... The problem of estimating the parameters of complexvalued sinusoidal signals (cisoids, for short) from data corrupted by colored noise occurs in many signal processing applications. We present a simple formula for the asymptotic (largesample) Cram'erRao bound (CRB) matrix associated with thi ..."
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Cited by 21 (2 self)
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with this problem. The maximum likelihood method (MLM), which estimates both the signal and noise parameters, attains the performance corresponding to the asymptotic CRB, as the sample length increases. More interestingly, we show that a computationally much simpler nonlinear leastsquares method (NLSM), which
CramérRao Lower Bounds for LowRank Decomposition of Multidimensional Arrays
 IEEE Trans. on Signal Processing
, 2001
"... Unlike lowrank matrix decomposition, which is generically nonunique for rank greater than one, lowrank threeand higher dimensional array decomposition is unique, provided that the array rank is lower than a certain bound, and the correct number of components (equal to array rank) is sought in the ..."
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Cited by 32 (5 self)
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, and compares alternating least squares algorithms that are commonly used to compute such decompositions with the respective CRBs. Simpletocheck necessary conditions for a unique lowrank decomposition are also provided. Index TermsCramrRao bound, least squares method, matrix decomposition
CramérRao type bounds for localization
 EURASIP Journal on Applied Signal Processing
"... for sensor networks. This paper studies the CramérRao lower bound (CRB) for two kinds of localization based on noisy range measurements. The first is Anchored Localization in which the estimated positions of at least 3 nodes are known in global coordinates. We show some basic invariances of the CRB ..."
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Cited by 1 (0 self)
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for sensor networks. This paper studies the CramérRao lower bound (CRB) for two kinds of localization based on noisy range measurements. The first is Anchored Localization in which the estimated positions of at least 3 nodes are known in global coordinates. We show some basic invariances
Recursive Algorithms for Computing the CramerRao Bound
 IEEE Trans. on Information Theory
"... Computation of the CramerRao bound (CRB) on estimator variance requires the inverse or the pseudoinverse Fisher information matrix (FIM). Direct matrix inversion can be computationally intractable when the number of unknown parameters is large. In this note we compare several iterative methods for ..."
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Cited by 20 (6 self)
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" algorithms. Key Words: Performance bounds, multidimensional parameter estimation, monotone matrix splitting iterations, GaussSeidel, preconditioned conjugate gradient. I. Introduction The CramerRao (CR) bound is a widely used lower bound on estimator covariance. When there are n unknown parameters
The StructureMapping Engine: Algorithm and Examples
 Artificial Intelligence
, 1989
"... This paper describes the StructureMapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structuremapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibili ..."
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Cited by 512 (115 self)
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flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structuremapping theory and describe the design of the engine. We analyze the complexity of the algorithm
A learning algorithm for Boltzmann machines
 Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
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Cited by 586 (13 self)
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The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 707 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Results 1  10
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