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482
Design and Analysis of the Progressive Second Price auction . . .
, 1999
"... We present the Progressive Second Price auction, a new decentralized mechanism for allocation of variablesize shares of a resource among multiple users. Unlike most mechanisms in the economics litterature, PSP is designed with a very small message space, making it suitable for realtime market pric ..."
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Cited by 61 (8 self)
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We present the Progressive Second Price auction, a new decentralized mechanism for allocation of variablesize shares of a resource among multiple users. Unlike most mechanisms in the economics litterature, PSP is designed with a very small message space, making it suitable for realtime market pricing of communication bandwidth. Under elastic demand, the PSP auction is incentive compatible and stable, in that it has a "truthful"Nash equilibrium where all players bid at prices equal to their marginal valuation of the resource. PSP is economically efficient in that the equilibrium allocation maximizes total user value. With simulations using a protype implementation of the auction game on the Internet, we investigate how convergence times scale with the number of bidders, as well as the tradeoff between engineering and economic efficiency. We also provide a ratedistortion
Support vector machine with adaptive parameters in financial time series forecasting
 IEEE Transactions on Neural Networks
, 2003
"... Abstract—A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper ..."
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Cited by 59 (1 self)
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Abstract—A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer backpropagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting. Index Terms—Backpropagation (BP) neural network, nonstationarity, regularized radial basis function (RBF) neural network, support vector machine (SVM). I.
TrustRegion InteriorPoint Algorithms For Minimization Problems With Simple Bounds
 SIAM J. CONTROL AND OPTIMIZATION
, 1995
"... Two trustregion interiorpoint algorithms for the solution of minimization problems with simple bounds are analyzed and tested. The algorithms scale the local model in a way similar to Coleman and Li [1]. The first algorithm is more usual in that the trust region and the local quadratic model are c ..."
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Cited by 55 (17 self)
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Two trustregion interiorpoint algorithms for the solution of minimization problems with simple bounds are analyzed and tested. The algorithms scale the local model in a way similar to Coleman and Li [1]. The first algorithm is more usual in that the trust region and the local quadratic model are consistently scaled. The second algorithm proposed here uses an unscaled trust region. A global convergence result for these algorithms is given and dogleg and conjugategradient algorithms to compute trial steps are introduced. Some numerical examples that show the advantages of the second algorithm are presented.
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
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Cited by 55 (6 self)
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A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
Reactive nonholonomic trajectory generation via parametric optimal control
 Int. J. Robot. Res
, 2003
"... There are many situations for which a feasible nonholonomic motion plan must be generated immediately based on realtime perceptual information. Parametric trajectory representations limit computation because they reduce the search space for solutions (at the cost of potentially introducing subopti ..."
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Cited by 54 (13 self)
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There are many situations for which a feasible nonholonomic motion plan must be generated immediately based on realtime perceptual information. Parametric trajectory representations limit computation because they reduce the search space for solutions (at the cost of potentially introducing suboptimality). The use of any parametric trajectory model converts the optimal control formulation into an equivalent nonlinear programming problem. In this paper, curvature polynomials of arbitrary order are used as the assumed form of solution. Polynomials sacrifice little in terms of spanning the set of feasible controls while permitting an expression of the general solution to the system dynamics in terms of decoupled quadratures. These quadratures are then readily linearized to express the necessary conditions for optimality. Resulting trajectories are convenient to manipulate and execute in vehicle controllers and they can be computed with a straightforward numerical procedure in real time. KEY WORDS—mobile robots, carlike robots, trajectory generation, curve generation, nonholonomic, clothoid, cornu spiral, optimal control
Market Mechanisms for Network Resource Sharing
, 1999
"... The theme of this thesis is the design and analysis of decentralized and distributed market mechanisms for resource sharing in multiservice networks. The motivation for a marketbased approach is twofold. First, in modern multiservice networks, resources such as bandwidth and buffer space have dif ..."
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Cited by 45 (7 self)
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The theme of this thesis is the design and analysis of decentralized and distributed market mechanisms for resource sharing in multiservice networks. The motivation for a marketbased approach is twofold. First, in modern multiservice networks, resources such as bandwidth and buffer space have different value to different users, and these valuations cannot, in general, be accurately known in advance as users compete against each other for the resources. Second, the network resources themselves are distributed, and often, not subject to any single authority. We present
Reinforcement Learning in Robotics: A Survey
"... Reinforcement learning offers to robotics a framework and set oftoolsfor the design of sophisticated and hardtoengineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between di ..."
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Cited by 39 (2 self)
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Reinforcement learning offers to robotics a framework and set oftoolsfor the design of sophisticated and hardtoengineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between modelbased and modelfree as well as between value functionbased and policy search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and
An Evolutionary Artificial Neural Networks Approach for Breast Cancer Diagnosis
 Artificial Intelligence in Medicine
, 2002
"... This paper presents an evolutionary artificial neural network approach based on the pareto differential evolution algorithm augmented with local search for the prediction of breast cancer. ..."
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Cited by 39 (6 self)
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This paper presents an evolutionary artificial neural network approach based on the pareto differential evolution algorithm augmented with local search for the prediction of breast cancer.
Flexible constrained spectral clustering
 in KDD, 2010
"... Constrained clustering has been wellstudied for algorithms like Kmeans and hierarchical agglomerative clustering. However, how to encode constraints into spectral clustering remains a developing area. In this paper, we propose a flexible and generalized framework for constrained spectral clusterin ..."
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Cited by 38 (4 self)
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Constrained clustering has been wellstudied for algorithms like Kmeans and hierarchical agglomerative clustering. However, how to encode constraints into spectral clustering remains a developing area. In this paper, we propose a flexible and generalized framework for constrained spectral clustering. In contrast to some previous efforts that implicitly encode MustLink and CannotLink constraints by modifying the graph Laplacian or the resultant eigenspace, we present a more natural and principled formulation, which preserves the original graph Laplacian and explicitly encodes the constraints. Our method offers several practical advantages: it can encode the degree of belief (weight) in MustLink and CannotLink constraints; it guarantees to lowerbound how well the given constraints are satisfied using a userspecified threshold; and it can be solved deterministically in polynomial time through generalized eigendecomposition. Furthermore, by inheriting the objective function from spectral clustering and explicitly encoding the constraints, much of the existing analysis of spectral clustering techniques is still valid. Consequently our work can be posed as a natural extension to unconstrained spectral clustering and be interpreted as finding the normalized mincut of a labeled graph. We validate the effectiveness of our approach by empirical results on realworld data sets, with applications to constrained image segmentation and clustering benchmark data sets with both binary and degreeofbelief constraints.