共查询到20条相似文献,搜索用时 156 毫秒
1.
提取图像中旋转不变特征是图像处理和模式识别中重要的应用。在极坐标下的正交矩函数则是提取这种特征信息的主要方法。正交矩函数在图像分解和重建过程中的误差是衡量其特征提取精确度的标准。为了提高正交矩函数在图像重建中的性能,提出了一种新的基于三角函数的正交矩函数和一种基于函数误差分析的新的衡量方法,并分别应用新的衡量方法和传统的在大量图像中进行重建误差统计的方法对新的正交矩函数以及另外两种在特征提取方面表现最好的正交矩函数进行了比较。实验结果验证了新的衡量方法的有效性并得到了新的正交矩函数的重建效果更好的结论。 相似文献
2.
3.
核方法是机器学习中一种新的强有力的学习方法。针对核方法进行了探讨,给出了核方法的基本思想和优点。同时,描述了核方法的算法实现并举例进行了说明。 相似文献
4.
5.
提出一种新的方法-基于法向量差值的区域生长,还提出了在候选种子中选择种子的方法,根据一种新的二面角的公式即法向量面积加权的差值进行区域生长,分割后对面积过小的面片区域进行优化处理。实验表明该方法快速有效。 相似文献
6.
7.
8.
9.
非接触式测量法是监测叶片振动的一项重要的新技术。为了解决非接触式测量中采样频率低的缺点,本文提出了一种新的跳越采样方法及其采样信号的数据处理方法,并通过模拟试验进行了验证。这种新的采样和信号处理方法可以在工程试验中推广应用。 相似文献
10.
11.
基于粗集理论的机器学习与推理 总被引:2,自引:0,他引:2
利用粗集理论探讨机器学习中的几个重要概念及研究方法,提出一种基于祖集理论的推理和学习方法。这种研究方法不仅开拓了一条机器学习的新途径,而且也是从数据中推理决策规则的一种新探索。 相似文献
12.
Fen Xia Author Vitae Yan-wu Yang Author Vitae Author Vitae Fuxin Li Author Vitae Author Vitae Daniel D. Zeng Author Vitae 《Pattern recognition》2009,42(7):1572-1581
In cost-sensitive learning, misclassification costs can vary for different classes. This paper investigates an approach reducing a multi-class cost-sensitive learning to a standard classification task based on the data space expansion technique developed by Abe et al., which coincides with Elkan's reduction with respect to binary classification tasks. Using this proposed reduction approach, a cost-sensitive learning problem can be solved by considering a standard 0/1 loss classification problem on a new distribution determined by the cost matrix. We also propose a new weighting mechanism to solve the reduced standard classification problem, based on a theorem stating that the empirical loss on independently identically distributed samples from the new distribution is essentially the same as the loss on the expanded weighted training set. Experimental results on several synthetic and benchmark datasets show that our weighting approach is more effective than existing representative approaches for cost-sensitive learning. 相似文献
13.
Yonghong Peng 《Journal of Intelligent Manufacturing》2004,15(3):373-380
Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques. 相似文献
14.
15.
16.
Lazy Learning of Bayesian Rules 总被引:19,自引:0,他引:19
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A number of approaches have sought to alleviate this problem. A Bayesian tree learning algorithm builds a decision tree, and generates a local naive Bayesian classifier at each leaf. The tests leading to a leaf can alleviate attribute inter-dependencies for the local naive Bayesian classifier. However, Bayesian tree learning still suffers from the small disjunct problem of tree learning. While inferred Bayesian trees demonstrate low average prediction error rates, there is reason to believe that error rates will be higher for those leaves with few training examples. This paper proposes the application of lazy learning techniques to Bayesian tree induction and presents the resulting lazy Bayesian rule learning algorithm, called LBR. This algorithm can be justified by a variant of Bayes theorem which supports a weaker conditional attribute independence assumption than is required by naive Bayes. For each test example, it builds a most appropriate rule with a local naive Bayesian classifier as its consequent. It is demonstrated that the computational requirements of LBR are reasonable in a wide cross-section of natural domains. Experiments with these domains show that, on average, this new algorithm obtains lower error rates significantly more often than the reverse in comparison to a naive Bayesian classifier, C4.5, a Bayesian tree learning algorithm, a constructive Bayesian classifier that eliminates attributes and constructs new attributes using Cartesian products of existing nominal attributes, and a lazy decision tree learning algorithm. It also outperforms, although the result is not statistically significant, a selective naive Bayesian classifier. 相似文献
17.
This paper proposes a new hybrid approach for recurrent neural networks (RNN). The basic idea of this approach is to train
an input layer by unsupervised learning and an output layer by supervised learning. In this method, the Kohonen algorithm
is used for unsupervised learning, and dynamic gradient descent method is used for supervised learning. The performances of
the proposed algorithm are compared with backpropagation through time (BPTT) on three benchmark problems. Simulation results
show that the performances of the new proposed algorithm exceed the standard backpropagation through time in the reduction
of the total number of iterations and in the learning time required in the training process. 相似文献
18.
匹配树和决策树方法识别英语句子中的BaseNP 总被引:2,自引:1,他引:1
提出了语料库和机器学习相结合的方法识别英语句子中的简单的、非递归的名词短语(BaseNP),在含有词性标注和BaseNP边界标注的训练语料中,抽取所有不同类型BaseNP短语对应的词性序列(BaseNP规则),通过规则排序和语方学知识,对其中正确率低且明显不符合语法的规则进行剔除,在识别时,采取规则匹配树的方法进行最大长度匹配,通过归纳机器学习C4.5自满引入上下文信息,由C4.5算法学习出有效( 相似文献
19.
20.
属性约简是粗糙集理论中的重要研究内容之一.但属性约简是一个NP难题,需要通过启发式知识实四。文中提出利用分辨矩阵求不同的条件属性组合相对于决策属性的正域的方法,并给出新的求核属性的方法。在此基础上,提出了一种利用分辨矩阵实现属性约简的新算法,该算法能快速求最少属性且实现简单,并实现了属性约简与规则提取的同步.最后通过实例证明了其正确性。 相似文献