排序方式: 共有7条查询结果,搜索用时 15 毫秒
1
1.
B.T. Luke 《SAR and QSAR in environmental research》2013,24(1):41-57
While quantitative structure-activity relationships attempt to predict the numerical value of the activities, it is found that statistically good predictors do not always do a good job of qualitatively determining the activity. This study shows how Fuzzy classifiers can be used to generate Fuzzy structure-activity relationships which can more accurately determine whether or not a compound will be highly inactive, moderately inactive or active, or highly active. Four examples of these classifiers are presented and applied to a well-studied activity dataset. 相似文献
2.
Testing analog and mixed-signal circuits is a costly task due to the required test time targets and high end technical resources. Indirect testing methods partially address these issues providing an efficient solution using easy to measure CUT information that correlates with circuit performances. In this work, a multiple specification band guarding technique is proposed as a method to achieve a test target of misclassified circuits. The acceptance/rejection test regions are encoded using octrees in the measurement space, where the band guarding factors precisely tune the test decision boundary according to the required test yield targets. The generated octree data structure serves to cluster the forthcoming circuits in the production testing phase by solely relying on indirect measurements. The combined use of octree based encoding and multiple specification band guarding makes the testing procedure fast, efficient and highly tunable. The proposed band guarding methodology has been applied to test a band-pass Butterworth filter under parametric variations. Promising simulation results are reported showing remarkable improvements when the multiple specification band guarding criterion is used. 相似文献
3.
Amir Averbuch Valery Zheludev Pekka Neittaanmäki Pekka Wartiainen Kari Huoman Kim Janson 《Applied Acoustics》2011,(1):22-34
We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate. 相似文献
4.
5.
We are considering the problem of multi-criteria classification. In this problem, a set of “if … then …” decision rules is used as a preference model to classify objects evaluated by a set of criteria and regular attributes. Given a sample of classification examples, called learning data set, the rules are induced from dominance-based rough approximations of preference-ordered decision classes, according to the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA). The main question to be answered in this paper is how to classify an object using decision rules in situation where it is covered by (i) no rule, (ii) exactly one rule, (iii) several rules. The proposed classification scheme can be applied to both, learning data set (to restore the classification known from examples) and testing data set (to predict classification of new objects). A hypothetical example from the area of telecommunications is used for illustration of the proposed classification method and for a comparison with some previous proposals. 相似文献
6.
基于集成学习提出了一种新的模糊分类规则的产生算法。将分类规则的前件、后件模糊化,在自适应提升(Adaptive Boosting,AdaBoost)算法的迭代中,调整训练实例的分布,利用遗传算法产生模糊分类规则。并在规则学习的适应度函数中引入训练实例的分布,使得模糊分类规则在产生阶段就考虑相互之间的协作,产生具有互补性的分类规则集。从而改善了模糊分类规则的整体识别能力,提高了分类识别精度。 相似文献
7.
In model-based clustering, a situation in which true class labels are unknown and that is therefore also referred to as unsupervised
learning, observations are typically classified by the Bayes modal rule. In this study, we assess whether alternative classifiers
from the classification or supervised-learning literature—developed for situations in which class labels are known—can improve
the Bayes rule. More specifically, we investigate the performance of bootstrap-based aggregate (bagging) rules after adapting
these to the model-based clustering context. It is argued that specific issues, such as the label-switching problem, have
to be carefully addressed when using bootstrap methods in model-based clustering. Our two Monte Carlo studies show that classification
based on the Bayes rule is rather stable and difficult to improve by bootstrap-based aggregate rules, even for sparse data.
An empirical example illustrates the various approaches described in this paper. 相似文献
1