首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   35篇
  国内免费   1篇
  完全免费   9篇
  自动化技术   45篇
  2016年   1篇
  2015年   3篇
  2014年   2篇
  2013年   6篇
  2012年   3篇
  2011年   2篇
  2010年   5篇
  2009年   5篇
  2008年   4篇
  2007年   4篇
  2006年   2篇
  2005年   1篇
  2004年   2篇
  2003年   3篇
  2001年   1篇
  2000年   1篇
排序方式: 共有45条查询结果,搜索用时 125 毫秒
1.
多分类器集成的汉语词义消歧研究   总被引:9,自引:0,他引:9  
词义消歧长期以来一直是自然语言处理中的热点和难题,集成方法被认为是机器学习研究的四大趋势之一.系统研究了9种集成学习方法在汉语词义消歧中的应用.9种集成方法分别是乘法规则、均值、最大值、最小值、多数投票、序列投票、加权投票、概率加权和单分类器融合,其中乘法规则、均值、最大值3种集成方法还未曾应用于词义消歧.选取支持向量机模型、朴素贝叶斯和决策树作为3个单分类器.在两个不同的数据集上进行了实验,其一是选自现代汉语语义标注语料库的18个多义词,其二是国际语义评测SemEval-2007的中英文对译选择词消歧任务.实验结果显示,首次在词义消歧中引入应用的3种集成方法乘法、均值、最大值有良好的性能表现,3种方法的消歧准确率均高于最佳单分类器SVM,而且优于其他6种集成方法.  相似文献
2.
优化分类型神经网络线性集成   总被引:8,自引:0,他引:8       下载免费PDF全文
王正群  陈世福  陈兆乾 《软件学报》2005,16(11):1902-1908
构造多神经网络集成系统,系统的输出由个体神经网络的输出线性加权产生.提出了一种度量个体神经网络在不同的权重下集成性能的判别函数,函数表示了由个体神经网络输出刻画的模式类内会聚性和类间散布性.应用遗传算法解决了求解最优个体网络集成权重问题.分析了该判别函数的合理性及其与Bayes决策规则的关系.用两个手写体汉字特征数据集和4个UCI数据库中的数据集比较了所提出的神经网络集成方法和其他几种神经网络集成方法的性能.  相似文献
3.
Is Combining Classifiers with Stacking Better than Selecting the Best One?   总被引:5,自引:0,他引:5  
We empirically evaluate several state-of-the-art methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to selecting the best classifier from the ensemble by cross validation. Among state-of-the-art stacking methods, stacking with probability distributions and multi-response linear regression performs best. We propose two extensions of this method, one using an extended set of meta-level features and the other using multi-response model trees to learn at the meta-level. We show that the latter extension performs better than existing stacking approaches and better than selecting the best classifier by cross validation.  相似文献
4.
Combining Classifiers with Meta Decision Trees   总被引:3,自引:0,他引:3  
The paper introduces meta decision trees (MDTs), a novel method for combining multiple classifiers. Instead of giving a prediction, MDT leaves specify which classifier should be used to obtain a prediction. We present an algorithm for learning MDTs based on the C4.5 algorithm for learning ordinary decision trees (ODTs). An extensive experimental evaluation of the new algorithm is performed on twenty-one data sets, combining classifiers generated by five learning algorithms: two algorithms for learning decision trees, a rule learning algorithm, a nearest neighbor algorithm and a naive Bayes algorithm. In terms of performance, stacking with MDTs combines classifiers better than voting and stacking with ODTs. In addition, the MDTs are much more concise than the ODTs and are thus a step towards comprehensible combination of multiple classifiers. MDTs also perform better than several other approaches to stacking.  相似文献
5.
Learning with partly labeled data   总被引:2,自引:0,他引:2  
Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of a classifier. This paper outlines the two main classes of learning methods to deal with partly labeled data: pre-labeling-based learning and semi-supervised learning. Concretely, we introduce and discuss three methods from each class. The first three ones are two-stage methods consisting of selecting the data to be labeled and then training the classifier using the pre-labeled and the originally labeled data. The last three ones show how labeled and unlabeled data can be combined in a symbiotic way during training. The empirical evaluation of these methods shows: (1) pre-labeling methods tend be better than semi-supervised learning methods, (2) both labeled and unlabeled have positive effect on the classification accuracy of each of the proposed methods, (3) the combination of all the methods improve the accuracy, and (4) the proposed methods compare very well with the state-of-art methods.  相似文献
6.
Molecular level diagnostics based on microarray technologies can offer the methodology of precise, objective, and systematic cancer classification. Genome-wide expression patterns generally consist of thousands of genes. It is desirable to extract some significant genes for accurate diagnosis of cancer because not all genes are associated with a cancer. In this paper, we have used representative gene vectors that are highly discriminatory for cancer classes and extracted multiple significant gene subsets based on those representative vectors respectively. Also, an ensemble of neural networks learned from the multiple significant gene subsets is proposed to classify a sample into one of several cancer classes. The performance of the proposed method is systematically evaluated using three different cancer types: Leukemia, colon, and B-cell lymphoma.  相似文献
7.
监督学习的发展动态   总被引:2,自引:0,他引:2       下载免费PDF全文
1 引言近10年来,机器学习领域的研究取得了突飞猛进的发展,首先表现在符号学习、计算学习理论、神经网络、统计方法、模式识别等的许多原来分离的研究团体,由于研究的进展与相互的交流走到了一起;其次,机器学习技术除了用于解决传统的学习问题,如语音识别、人脸识别、手写体汉字识别、医  相似文献
8.
基于相反分类器的数据流分类方法   总被引:2,自引:0,他引:2       下载免费PDF全文
王勇  李战怀  张阳  蒋芸 《计算机科学》2006,33(8):206-209
目前挖掘概念流动的数据流已经成为研究的热点。概念流动的数据流分类在预防信用卡欺诈,网络入侵发现等应用中具有重要的应用。本文定义了一种相反分类器来从错误中学习,提出了训练一个集合分类器来对具有概念流动的数据流进行分类的算法IWB。通过在合成数据集和benchmark上的实验,与Weighted Baggging算法比较,表明我们的算法具有更高的准确度,更快地收敛到新的目标概念的性能。  相似文献
9.
A data driven ensemble classifier for credit scoring analysis   总被引:1,自引:0,他引:1  
This study focuses on predicting whether a credit applicant can be categorized as good, bad or borderline from information initially supplied. This is essentially a classification task for credit scoring. Given its importance, many researchers have recently worked on an ensemble of classifiers. However, to the best of our knowledge, unrepresentative samples drastically reduce the accuracy of the deployment classifier. Few have attempted to preprocess the input samples into more homogeneous cluster groups and then fit the ensemble classifier accordingly. For this reason, we introduce the concept of class-wise classification as a preprocessing step in order to obtain an efficient ensemble classifier. This strategy would work better than a direct ensemble of classifiers without the preprocessing step. The proposed ensemble classifier is constructed by incorporating several data mining techniques, mainly involving optimal associate binning to discretize continuous values; neural network, support vector machine, and Bayesian network are used to augment the ensemble classifier. In particular, the Markov blanket concept of Bayesian network allows for a natural form of feature selection, which provides a basis for mining association rules. The learned knowledge is represented in multiple forms, including causal diagram and constrained association rules. The data driven nature of the proposed system distinguishes it from existing hybrid/ensemble credit scoring systems.  相似文献
10.
基于实例加权方法的概念漂移问题研究   总被引:1,自引:0,他引:1       下载免费PDF全文
数据流上的漂移概念发现已成为数据挖掘领域的研究热点之一。针对存在概念漂移的数据流分类问题,提出一种基于实例加权方法的数据流分类算法(EWAMDS),根据基分类器在训练实例上的分类结果调整该实例的权值,以增强漂移实例在新分类器中的影响,同时引入动态的权值修改因子以提高算法的适应性。实验结果表明,动态地调整实例的权值时算法的适应性更强;与weighted-bagging相比,EWAMDS的时间开销显著降低、分类正确率显著提高。  相似文献
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号