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1.
支持向量机方法与模糊系统   总被引:12,自引:1,他引:11  
概括介绍了近年来倍受瞩目的一种新的计算机学习方法——支持向量机(Support Vector Machines,简称SVM)方法,这一方法具有坚实的理论基础和出色的应用效果;并分析了SVM方法与模糊系统的关系,对这两种方法的交互促进和发展提出了看法。  相似文献   

2.
支持向量机在近十年成为机器学习的主要学习技术,而且已经成功应用到有监督学习问题中。Fung和Mangasarian利用支持向量机对于既有已标类别样本又有未知类别样本的训练集进行训练,方法主要是利用少量已标明类别的样本进行训练得到一个分类器的同时对于未标明类别的样本进行分类,使得间隔最大化。此优化问题中假定样本是精确的,而在现实生活中,样本通常带有统计误差。因此,考虑样本带有扰动信息的半监督两类分类问题,给出鲁棒半监督v-支持向量分类算法。该算法的参数v易于选择,而数值试验也表明该算法具有良好的稳定性和较好的分类结果。  相似文献   

3.
Uncertainty is a concept associated with data acquisition and analysis, usually appearing in the form of noise or measure error, often due to some technological constraint. In supervised learning, uncertainty affects classification accuracy and yields low quality solutions. For this reason, it is essential to develop machine learning algorithms able to handle efficiently data with imprecision. In this paper we study this problem from a robust optimization perspective. We consider a supervised learning algorithm based on generalized eigenvalues and we provide a robust counterpart formulation and solution in case of ellipsoidal uncertainty sets. We demonstrate the performance of the proposed robust scheme on artificial and benchmark datasets from University of California Irvine (UCI) machine learning repository and we compare results against a robust implementation of Support Vector Machines.  相似文献   

4.
5.
In this paper we describe a proximal Support Vector Machine algorithm for multiclassification problem by one-vs-all scheme. The computational requirement for the new algorithm is almost the same as training one of its element binary proximal Support Vector Machines. Low rank approximation is taken to reduce computational costs when the kernel matrix is too large. An error bound estimation for the approximated solution is given, which is used as a stopping criteria for low rank approximation. A post-processing strategy is developed to overcome the difficulty arising from unbalanced data and to improve the classification accuracy. A parallel implementation of the algorithm using standard MPI communication routines is provided to handle large-scale problems and to accelerate the training process. Experiment results on several public datasets validate the effectiveness of our proposed algorithm.  相似文献   

6.
This paper presents an integrated approach for portfolio selection in a multicriteria decision making framework. Firstly, we use Support Vector Machines for classifying financial assets in three pre-defined classes, based on their performance on some key financial criteria. Next, we employ Real-Coded Genetic Algorithm to solve a mathematical model of the multicriteria portfolio selection problem in the respective classes incorporating investor-preferences.  相似文献   

7.
基于支持向量机的拟南芥基因表达数据分析   总被引:2,自引:0,他引:2  
针对拟南芥根部基因表达数据分析的问题,本文提出了一种新的基于距离度量学习的支持向机多分类算法.鉴于此问题的特殊性,本文通过最小化4分类机的LOO 误差来求得一个恰当的距离度量.并在此度量下找到若干个属于第5类(其它类)的训练点,从而构造出一个5分类机用来对所有基因分类.实验验证了此算法的可行性,并且比基因表达分析中传统使用的聚类方法更有效.  相似文献   

8.
Support Vector Machines (SVMs) is known to be a powerful nonparametric classification technique even for high-dimensional data. Although predictive ability is important, obtaining an easy-to-interpret classifier is also crucial in many applications. Linear SVM provides a classifier based on a linear score. In the case of functional data, the coefficient function that defines such linear score usually has many irregular oscillations, making it difficult to interpret.  相似文献   

9.
Classification problems with multiple classes suppose a challenge in Data Mining tasks. There is a difficulty inherent to the learning process when trying to find the most adequate discrimination functions among the different concepts within the dataset. Using Fuzzy Rule Based Classification Systems in general, and Evolutionary Fuzzy Systems in particular, provide the advantage of describing smoother borderline areas, thanks to the linguistic label-based representation.In multi-classification, the pairwise learning approach (One-vs-One) has gained a notorious attention. However, there is certain dependence between the goodness of the confidence degrees or scores of binary classifiers, and the final performance shown by the global model. Regarding this fact, the problem of non-competent classifiers is of special relevance. It occurs when a binary classifier outputs a positive score for a couple of classes unrelated with the input example, which may degrade the final accuracy. Precisely, the previously exposed properties of fuzzy classifiers make them more prone to the former condition.In this paper, we propose an extension of the distance-based combination strategy to overcome this non-competence problem. It is based on the truncation of the confidence degrees of the classes prior to the distance-based tuning. This allows taking advantage of the good classification abilities of Evolutionary Fuzzy Systems, while diminishing the adverse effect of the aforementioned non-competence. Experimental results, using FARC-HD with overlap functions as the fuzzy learning algorithm, show that this new adaptation of the Distance-based Relative Competence Weighting model outperforms both the OVO and standard distance-based approaches, and it is competitive with robust classifiers such as Support Vector Machines.  相似文献   

10.
In this paper we borrow concepts from Information Theory and Statistical Mechanics to perform a pattern recognition procedure on a set of X-ray hazelnut images. We identify two relevant statistical scales, whose ratio affects the performance of a machine learning algorithm based on statistical observables, and discuss the dependence of such scales on the image resolution. Finally, by averaging the performance of a Support Vector Machines algorithm over a set of training samples, we numerically verify the predicted onset of an “optimal” scale of resolution, at which the pattern recognition is favoured.  相似文献   

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