检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

L₁-regularization path algorithm for generalized linear models

引用

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

来源：评论

学校读者我要写书评

暂无评论

path algorithms for One-Class SVM

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

THE SOLUTION path OF THE GENERALIZED LASSO

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Modelling Interactions in High-dimensional Data with Backtracking

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2016年第1期17卷 1-31页

作者： Shah, Rajen D. Univ Cambridge Stat Lab Cambridge CB3 0WB England

We study the problem of high-dimensional regression when there may be interacting variables. Approaches using sparsity-inducing penalty functions such as the Lasso can be useful for producing interpretable models. However, when the number variables runs into the thousands, and so even two-way interactions number in the millions, these methods may become computationally infeasible. Typically variable screening based on model fits using only main effects must be performed first. One problem with screening is that important variables may be missed if they are only useful for prediction when certain interaction terms are also present in the model. To tackle this issue, we introduce a new method we call Backtracking. It can be incorporated into many existing high-dimensional methods based on penalty functions, and works by building increasing sets of candidate interactions iteratively. Models fitted on the main effects and interactions selected early on in this process guide the selection of future interactions. By also making use of previous fits for computation, as well as performing calculations is parallel, the overall run-time of the algorithm can be greatly reduced. The effectiveness of our method when applied to regression and classification problems is demonstrated on simulated and real data sets. In the case of using Backtracking with the Lasso, we also give some theoretical support for our procedure.

关键词： high-dimensional data interactions Lasso path algorithm

来源：评论

学校读者我要写书评

暂无评论

Characterization of the solutions set of the generalized LASSO problems for non-full rank cases

引用

ELECTRONIC JOURNAL OF STATISTICS 2023年第1期17卷 1663-1710页

作者： Hwang, Jaesung Won, Joongho Kim, Yongdai SKTelecom Seoul South Korea Seoul Natl Univ Dept Stat Seoul South Korea

Generalized Lasso has received much attention for sparse learning in statistics and machine learning, since it extends many existing sparse learning methods and provides estimators with flexible sparsity structures. When the design matrix is rank-deficient, however, the objective function of generalized Lasso may not be strictly convex, in which case multiple solutions exist. In practice, a small l2 penalty is added to the objective function to make it strictly convex but its validity is largely unknown. In this paper, we study the solutions set of the G-Lasso problem with a rank-deficient design matrix. The main contributions of this paper are (1) to develop a solution path algorithm to find an exact solution even when the design matrix is rank-deficient and (2) to characterize the solutions set by a linear subspace with linear inequalities. Using the solution path algorithm and the characterization of the solutions set, we explain how to find a solution with specific properties, such as one with the least l & INFIN;norm. By analyzing simulated and real data sets, we illustrate that there exist quite different multiple solutions and finding a solution with a specific property is important to have better accuracy as well as better interpretability. In addition, we show that the approach of adding a small l2 penalty to the objective function is sometimes numerically unstable and the resulting estimator is inaccurate.

关键词： Generalized lasso characterization convex op-timization path algorithm

来源：评论

学校读者我要写书评

暂无评论

Sparse Dictionary Learning for Blind Hyperspectral Unmixing

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2019年第4期16卷 578-582页

作者： Liu, Yang Guo, Yi Li, Feng Xin, Lei Huang, Puming Xian Inst Space Radio Technol CAST Xian 710100 Shaanxi Peoples R China Qian Xuesen Lab Space Technol Beijing 100094 Peoples R China Western Sydney Univ Sch Comp Engn & Math Parramatta NSW 2150 Australia

Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity. To solve this problem, this paper proposes an efficient algorithm to find all paths of the l(1)-regularization problem and select the best set of variables for the final abundances estimation. Based on the proposed algorithm, a DL framework is designed for hyperspectral unmixing. Our experimental results indicate that our method performs much better than conventional methods in terms of DL and hyperspectral data reconstruction. More importantly, it alleviates the difficulty of prescribing the sparsity.

关键词： Dictionary learning (DL) hyperspectral unmixing l(1)-regularization path algorithm sparse coding

来源：评论

学校读者我要写书评

暂无评论

Topology theory of mine ventilation network

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：