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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Peng, Pai Zhu, Yifan Hou, Hongtao Zhang, Wangxun Natl Univ Def Technol Inst Informat Syst & Management Changsha Hunan Peoples R China

ISBN: (纸本)9783037856932

It is a problem to build Inter Satellite Links so that Region Navigation System has optimal performance. Therefore, the article puts forward an algorithm, which synthesizes the time of satellites accessed and the distance of satellites. And then it carries out a simulation in navigation system to certify the superiority of the algorithm. The experimental result shows that the ISLs can effectively improve the performance of navigation system. And comparing with the traditional Minimum Distance algorithm, the proposed algorithm has the same effect on improving the performance of navigation system and needs fewer times of changing links.

关键词： Inter Satellite Links path algorithm Ranging Access Dilution of Precision

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THE SOLUTION path OF THE GENERALIZED LASSO

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ANNALS OF STATISTICS 2011年第3期39卷 1335-1371页

作者： Tibshirani, Ryan J. Taylor, Jonathan Stanford Univ Dept Stat Stanford CA 94305 USA

We present a path algorithm for the generalized lasso problem. This problem penalizes the l(1) norm of a matrix D times the coefficient vector, and has a wide range of applications, dictated by the choice of D. Our algorithm is based on solving the dual of the generalized lasso, which greatly facilitates computation of the path. For D = I (the usual lasso), we draw a connection between our approach and the well-known LARS algorithm. For an arbitrary D, we derive an unbiased estimate of the degrees of freedom of the generalized lasso fit. This estimate turns out to be quite intuitive in many applications.

关键词： Lasso path algorithm Lagrange dual LARS degrees of freedom

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A path algorithm for the support vector domain description and its application to medical imaging

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MEDICAL IMAGE ANALYSIS 2007年第5期11卷 417-428页

作者： Sjostrand, Karl Hansen, Michael Sass Larsson, Henrik B. Larsen, Rasmus Tech Univ Denmark Informat & Math Modelling Lyngby Denmark Glostrup Cty Hosp Dept Clin Physiol & Nucl Med Glostrup Denmark

The support vector domain description is a one-class classification method that estimates the distributional support of a data set. A flexible closed boundary function is used to separate trustworthy data on the inside from outliers on the outside. A single regularization parameter determines the shape of the boundary and the proportion of observations that are regarded as outliers. Picking an appropriate amount of regularization is crucial in most applications but is, for computational reasons, commonly limited to a small collection of parameter values. This paper presents an algorithm where the solutions for all possible values of the regularization parameter are computed at roughly the same computational complexity previously required to obtain a single solution. Such a collection of solutions is known as a regularization path. Knowledge of the entire regularization path not only aids model selection, but may also provide new information about a, data set. We illustrate this potential of the method in two applications;one where we establish a sensible ordering among a set of corpora callosa outlines, and one where ischemic segments of the myocardium are detected in patients with acute myocardial infarction. (C) 2007 Elsevier B.V. All rights reserved.

关键词： support vector domain description novelty detection outlier detection classification path algorithm corpus callosum myocardial perfusion ischemic segment detection

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L₁-regularization path algorithm for generalized linear models

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2007年第4期69卷 659-677页

作者： Park, Mee Young Hastie, Trevor Google Inc Mountain View CA 94043 USA Stanford Univ Stanford CA 94305 USA

We introduce a path following algorithm for L-1-regularized generalized linear models. The L-1-regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L-1-norm of the coefficients, in a manner that is less greedy than forward selection-backward deletion. The generalized linear model path algorithm efficiently computes solutions along the entire regularization path by using the predictor-corrector method of convex optimization. Selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths;we suggest intuitive and flexible strategies for choosing appropriate values. We demonstrate the implementation with several simulated and real data sets.

关键词： generalized linear model lasso path algorithm predictor-corrector method regularization variable selection

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Nearly-Isotonic Regression

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TECHNOMETRICS 2011年第1期53卷 54-61页

作者： Tibshirani, Ryan J. Hoefling, Holger Tibshirani, Robert Stanford Univ Dept Stat Stanford CA 94305 USA Novartis Mol Diagnost CH-4002 Basel Switzerland Stanford Univ Dept Hlth Stanford CA 94305 USA Stanford Univ Dept Res & Policy Stanford CA 94305 USA Stanford Univ Dept Stat Stanford CA 94305 USA

We consider the problem of approximating a sequence of data points with a "nearly-isotonic," or nearly-monotone function. This is formulated as a convex optimization problem that yields a family of solutions, with one extreme member being the standard isotonic regression fit. We devise a simple algorithm to solve for the path of solutions, which can be viewed as a modified version of the well-known pool adjacent violators algorithm, and computes the entire path in O(n log n) operations (n being the number of data points). In practice, the intermediate fits can be used to examine the assumption of monotonicity. Nearly-isotonic regression admits a nice property in terms of its degrees of freedom: at any point along the path, the number of joined pieces in the solution is an unbiased estimate of its degrees of freedom. We also extend the ideas to provide "nearly-convex" approximations.

关键词： Degrees of freedom Isotonic regression path algorithm Pool adjacent violators

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The Study and algorithm Implement of Multi-constrained Optimal path Problem

The Study and Algorithm Implement of Multi-constrained Optim...

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第三届国际机械工程与力学会议

作者： Haimei WANG Yueyong MA Dept.of Automation Nanjing University of Science and Technology Modern Educational Technology Cente Nanjing University of Science and Technology

Multi-constrained optimal path （MCOP） problem which belongs to the path planning widely exists in many fields such as intelligent transportation systems, computer network and communication systems. Based on the network with multiple constraints on each link, the paper conducted an in-depth study on model and algorithm of the MCOP problem, which includes: Establish a MCOP problem model based on road transportation network;Propose a MCOP algorithm;By introducing heuristic idea into the research of MCOP problem, design an exact A*COP algorithm;Prove through experiments the validity of the two algorithms and the superiority of A*COP algorithm.

关键词： multi-constrained optimal path path planning network model path algorithm

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Topology theory of mine ventilation network

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Procedia Earth and Planetary Science 2009年第1期1卷 354-360页

作者： Lian-jiang, Wei Faculty of Safety Engineering China University of Mining and Technology Xuzhou 221116 China Jiangsu Key Laboratory of Resource and Environmental Information Engineering China University of Mining and Technology Xuzhou 221008 China

Topological theory of ventilation network is the basis of mine ventilation simulation system. But there still exists disparity and insufficiency between theory and application. On the basis of in-depth study of ventilation network topological theory, the concept of the real and the simplest fuzzy topological ventilation network are put forward, and the reason of the occurrence of reverse branches after network calculation is explained on the aspects of topological theory of ventilation network. We study the automatic generation of the topological relationship and automatic simplification of complicated ventilation network. Through simulation of tree growth, we develop a simple, effective and adaptable method, the "path Tree Depth-first Growth" method, which can be used to search all paths of two arbitrary nodes. Different from previous research, the simplified theory of ventilation network and the path algorithm are simple, effective and adaptable, which are important to theory research and program design. © 2009.

关键词： Computer simulation Mine ventilation Mining Topology Automatic Generation In depth study Mine ventilation network path algorithm Program design simplified Simulation systems Topological relationships Topological theory Topology theory Tree growth Tree depth Ventilation network

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path algorithms for One-Class SVM

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5th International Symposium on Neural Networks

作者： Zhou, Liang Li, Fuxin Yang, Yanwu Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783540877318

The One-Class Support Vector Machine (OC-SVM) is all unsupervised learning algorithm, identifying unusual or outlying points (outliers) from a given dataset. In OC-SVM, it is required to set the regularization hyperparameter and kernel hyperparameter in order to obtain a good estimate. Generally, cross-validation is often used which requires multiple runs with different hyperparameters, making it very slow. Recently, the solution path algorithm becomes popular. It can obtain every solution for all hyperparameters in a single run rather than re-solve the optimization problem multiple times. Generalizing from previous algorithms for solution path in SVMs, this paper proposes a complete set of solution path algorithms for OC-SVM, including a v-path algorithm and a kernel-path algorithm. In the kernel-path algorithm, a new method is proposed to avoid the failure of algorithm due to indefinite matrix. Using those algorithms, we call obtain the optimum hyperparameters by computing all entire path solution with the computational cost O(n(2) + cnm(3)) on v-path algorithm or O(cn(3) + cnm(3)) on kernel-path algorithm or (c: constant, n: the number of sample, m: the number of sample which on the margin).

关键词： path algorithm One-Class SVM Regularization Kernel

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A density-dependent stand-level optimization approach for deriving management prescriptions for interior northwest (USA) landscapes

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FOREST ECOLOGY AND MANAGEMENT 2005年第2-3期217卷 171-186页

作者： Bettinger, P Graetz, D Sessions, J Univ Georgia Warnell Sch Foest Resources Athens GA 30602 USA Oregon State Univ Dept Forest Resources Corvallis OR 97331 USA Oregon State Univ Dept Forest Engn Corvallis OR 97331 USA

Stand-level forest management prescriptions for federal forests in the interior northwest (USA) have changed emphasis in the past decade from being influenced by economic criteria to being influenced by ecological criteria. The design of forest management prescriptions that maintain stand density levels within a target range is now preferred over the design of prescriptions that maximize net present value. We describe a stand-level optimization process for developing efficient management regimes that uses dynamic programming and a region-limited search strategy. The process develops management regimes by penalizing deviations from a preferred range of stand density. Operational considerations, such as minimum harvest levels, can be important, and are considered as constraints in this modeling process. The timing of harvests is such that entries are only permitted when a minimum harvest can be obtained, when a minimum residual basal area can be maintained, and when harvests are limited to trees with a certain diameter range. Lists of residual, harvested, and dead tree records are available for each decade of the optimal regime. These records of information can facilitate further forest product or habitat suitability analysis associated with forest policy analyses. (c) 2005 Elsevier B.V. All rights reserved.

关键词： mixed conifer stand types data management path algorithm region-limited strategy

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