Exponentially weighted moving average ( ewma ) and fuzzy algorithm for the input samples are also developed to improve its recognition accuracy . numerical simulation results show this model possesses many advantages , such as good self - adaptive ability , quick training and good recognition performance 文中提出了采用欧氏距离判别法作为混合型多特征异常模式的识别方法;提出了采用数据模糊化和指数加权滑动平均处理两种提高模型识别精度的方法。
The paper take email the one kind of common digital information mediums as the research object . it realizes the intelligent processing of the mail digital information through the research of mail digital information processing based on machine learning . it enhances the classification and recognition accuracy of the mail digital information in order to satisfy the request that the mail gateway accurately filters the spam mail 本文以电子邮件这种常见的数字信息媒介为研究对象,通过基于机器学习的邮件数字信息处理技术研究,实现对邮件数字信息的智能处理,提高邮件数字信息分类识别的准确性,以满足邮件网关对垃圾邮件准确过滤的要求。
Then , lda is followed and employed for the second feature extraction in the transformed space . based on this framework , a combinatorial optimal lda ( olda ) algorithm is proposed . experiments is performed on orl face image database as well as nust603 face image database , and the experimental results indicate that olda is robust , efficient and superior to the previous lda - based methods in terms of recognition accuracy 进一步地,在该理论框架下,具体给出了处理高维、小样本问题的组合最优的线性鉴别分析算法,在orl标准人脸库和nust603人脸库上的试验结果证实了该方法的卓越性能,在识别率方面均优于以往的同类方法。
At the present time the prediction method of attracting pest with black light and recognizing and counting by man is generally adopted . there are some serious shortages such as bad recognition accuracy and low efficiency . it reduces seriously accuracy and timeliness of prediction and is disadvantage in guiding insect disease prevention 目前普遍采用的黑光灯诱集害虫、人工识别计数的测报方法,存在识别准确性差、效率低等严重缺陷,极大地降低了测报的准确度和时效性,不利于指导农田害虫的防治工作,因此本文提出了基于机器视觉和小波分析的图像识别技术,用于农田害虫的自动检测预报。