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排序方式: 共有74条查询结果,搜索用时 38 毫秒
1.
A Tutorial on the Cross-Entropy Method   总被引:30,自引:0,他引:30  
The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss applications in combinatorial optimization and machine learning.  相似文献
2.
Multicategory Classification by Support Vector Machines   总被引:7,自引:0,他引:7  
We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik's Support Vector Machine (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise-linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise-nonlinear classification function. Each piece of this function can take the form of a polynomial, a radial basis function, or even a neural network. For the k > 2-class problems, the SVM method as originally proposed required the construction of a two-class SVM to separate each class from the remaining classes. Similarily, k two-class linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach.  相似文献
3.
模糊规则提取的两种方法性能分析   总被引:5,自引:0,他引:5  
机器学习近年来得到越来越多的重视,模糊规则提取是其中的重要的一个方向。本文介绍了两种自动提取模糊规则的方法,分别是基于多层前向网络和基于遗传算法的模糊规则自动生成。并且,详细的分析了两种方法性能  相似文献
4.
Rainfall forecasting by technological machine learning models   总被引:5,自引:0,他引:5  
Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. Recurrent artificial neural networks (RNNS) have played a crucial role in forecasting rainfall data. Meanwhile, support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. This investigation elucidates the feasibility of hybrid model of RNNs and SVMs, namely RSVR, to forecast rainfall depth values. Moreover, chaotic particle swarm optimization algorithm (CPSO) is employed to choose the parameters of a SVR model. Subsequently, example of rainfall values during typhoon periods from Northern Taiwan is used to illustrate the proposed RSVRCPSO model. The empirical results reveal that the proposed model yields well forecasting performance, RSVRCPSO model provides a promising alternative for forecasting rainfall values.  相似文献
5.
A new family of proximity graphs: Class cover catch digraphs   总被引:1,自引:0,他引:1  
Motivated by issues in machine learning and statistical pattern classification, we investigate a class cover problem (CCP) with an associated family of directed graphs—class cover catch digraphs (CCCDs). CCCDs are a special case of catch digraphs. Solving the underlying CCP is equivalent to finding a smallest cardinality dominating set for the associated CCCD, which in turn provides regularization for statistical pattern classification. Some relevant properties of CCCDs are studied and a characterization of a family of CCCDs is given.  相似文献
6.
具有一般学习效应的单机排序问题   总被引:1,自引:0,他引:1       下载免费PDF全文
在具有学习效应的环境下,由于机器重复加工相同或相似的工件,因此以后加工的工件的加工时间变小.本文研究新的更一般的学习效应:Dejong学习效应.我们证明单机最大完工时间问题,总完工时间问题和两类多目标问题是多项式时间可解的.  相似文献
7.
Design of fuzzy radial basis function-based polynomial neural networks   总被引:1,自引:0,他引:1  
In this study, we introduce a new design methodology of fuzzy radial basis function-based polynomial neural networks. In many cases, these models do not come with capabilities to deal with granular information. With this regard, fuzzy sets offer several interesting and useful opportunities. This study presents the development of fuzzy radial basis function-based neural networks augmented with virtual input variables. The performance of the proposed category of models is quantified through a series of experiments, in which we use two machine learning data sets and two publicly available software development effort data.  相似文献
8.
A due-date assignment problem with learning effect and deteriorating jobs   总被引:1,自引:0,他引:1  
In this paper we consider a single-machine scheduling problem with the effects of learning and deterioration. In this model, job processing times are defined by functions of their starting times and positions in the sequence. The problem is to determine an optimal combination of the due-date and schedule so as to minimize the sum of earliness, tardiness and due-date. We show that the problem remains polynomially solvable under the proposed model.  相似文献
9.
In several application domains such as biology, computer vision, social network analysis and information retrieval, multi-class classification problems arise in which data instances not simply belong to one particular class, but exhibit a partial membership to several classes. Existing machine learning or fuzzy set approaches for representing this type of fuzzy information mainly focus on unsupervised methods. In contrast, we present in this article supervised learning algorithms for classification problems with partial class memberships, where class memberships instead of crisp class labels serve as input for fitting a model to the data. Using kernel logistic regression (KLR) as a baseline method, first a basic one-versus-all approach is proposed, by replacing the binary-coded label vectors with [0,1]-valued class memberships in the likelihood. Subsequently, we use this KLR extension as base classifier to construct one-versus-one decompositions, in which partial class memberships are transformed and estimated in a pairwise manner. Empirical results on synthetic data and a real-world application in bioinformatics confirm that our approach delivers promising results. The one-versus-all method yields the best computational efficiency, while the one-versus-one methods are preferred in terms of predictive performance, especially when the observed class memberships are heavily unbalanced.  相似文献
10.
There is lot of excitement with Pattern Recognition methods with high precision, since this problem area is a well-established field of Operations Research (O.R.). Recent work of some researchers has shown that O.R. methods in general and Optimisation methods in particular, can be applied to give some very good results. Thus this research area has been won back from the Artificial Intelligence community and is quickly becoming once more a fast growing field in O.R. The aim of this review is to examine the early success of classification methods and Pattern Recognition methods, consider their downfall and examine the new techniques that have been applied to make it like a resurgent Phoenix. It will be shown that optimisation methods, if carried out properly, through a formal analysis of their structure and their requirements can achieve correct classification with probability one. Many researchers make it more difficult for themselves by not considering the formalisation of the task concerned and so adapt heuristics to the problem. Computational methods taken from the Irvine Repository database on recognition instances will be placed in evidence. The outline of the paper is as follows. After the introduction a historical sketch of the field will be presented. Then in Section 3, the need for formal methods will be argued and various results on formal requirements as convergence etc. will be derived. Many of these formal requirements are of course related to the best-unbiased estimate (b.u.e) requirements in Statistics. In Section 4 some popular algorithms for Pattern Recognition will be presented and their degree of satisfaction of the formal requirements stressed, allowing in Section 5 to present many applications, so that conclusions can be reached in Section 6. It will be found that the satisfaction of the formal requirements is a necessary and sufficient condition to reach recognition with probability one.  相似文献
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