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人脸识别是机器视觉和模式识别领域最富有挑战性的课题之一，同时也具有较为广泛的应用意义。人脸识别技术是一个非常活跃的研究领域，它覆盖了数字图像处理、模式识别、计算机视觉、神经网络、心理学、生理学、数学等诸多学科的内容。如今，虽然在这方面的研究已取得了一些可喜的成果-Face recognition is the field of machine vision and pattern recognition, one of the most challenging issues, but also
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各种分类器的介绍，算法的研究论文，还有模式识别的基础知识-Introduction of various classifiers, the algorithm research paper, as well as the basics of pattern recognition
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本文提出特征参数统计值的概念，即对连续的多段信
特征参数后，所求取的多组参数中各参数的最大值、最小值及二
以此作为输入参数，利用基于BP神经网络和决策理论的模式识
星统一载波测控体制中典型的调制方式进行了识别，包括2FSK
)、4FSK、BPSK、QPSK、MSK、PM、CW-In this paper, the concept of characteristic parameters of statistics, that the letter of the continuou
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模式识别课件 哈尔滨工业大学模式识别的入门课程 -Pattern recognition introductory courses in pattern recognition courseware Harbin Institute of Technology
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模式识别中的模糊聚类，采用K-L信息作为先验概率，高水平文章。-Pattern recognition, fuzzy clustering, using KL information as a priori probability, high-level posts.
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模式识别中的模糊聚类，没有采用传统的高斯分布，而是采用学生t分布，具有重尾特性，识别率更高。-Pattern recognition, fuzzy clustering, without using the traditional Gaussian distribution, instead of using the Student' s t distribution with heavy-tailed characteristics, the recognition rate highe
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模式识别中的模糊聚类，比较经典的文章，考虑了本地信息的FCM。-Pattern recognition, fuzzy clustering, more classic article, considering local information FCM.
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模式识别中的模糊聚类，采用隐马尔科夫链，考虑了先验概率，进行图像识别。-Pattern recognition, fuzzy clustering, using hidden Markov chain, taking into account a priori probability for image recognition.
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In this paper, we propose a new robust
AMC algorithm, which applies higher-order statistics (HOS) in
a generic framework for blind channel estimation and pattern
recognition
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人脸的研究是跨越人文科学与自然科学的新兴交叉研究领域，在最近几年得到了模式识别领域众多学者的重视，也取得了良好的研究成果。针对人脸这种生物特征的识别，在其过程中最为重要的一个环节是特征的提取，更好的提取出人脸的特征，将会使得识别更有有效和准确，提高分类的同时，也是的识别率有良好的提高。-Face recognition research is a new cross field across the humanities and natural science, had many scholar
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关于模式识别的参考文献，有LBP（局部二值模式），BLBP（多尺度二值模式），MBE-LBP等多种算法，可以看考一下，里面的内容是有关于人脸的识别。-References about pattern recognition, with LBP (local binary pattern), BLBP (multi-scale binary mode), MBE- LBP and other algorithm, can look to take an examination of, it is
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Template matching is concerned with measuring the similarity
between patterns of two objects. This paper proposes a massively
parallel approach to pattern recognition with a large
template set. A class of image recognition problems inherently
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有详细的注释，解耦，恢复原信号，可以实现模式识别领域的数据的分类及回归。- There are detailed notes, Decoupling, restore the original signal, You can achieve data classification and regression pattern recognition.
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BP神经网络用于函数拟合与模式识别，关于非线性离散系统辨识，对HARQ系统的吞吐量分析。- BP neural network function fitting and pattern recognition, Nonlinear discrete system identification, HARQ throughput analysis of the system.
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BP神经网络用于函数拟合与模式识别，基于SVPWM的三电平逆变的matlab仿真，包括压缩比、运行时间和计算复原图像的峰值信噪比。- BP neural network function fitting and pattern recognition, Based on SVPWM three-level inverter matlab simulation, Including compression ratio, image restoration computing uptime and
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单径或多径瑞利衰落信道仿真，可以广泛的应用于数据预测及数据分析,模式识别中的bayes判别分析算法。- Single path or multipath Rayleigh fading channel simulation, Can be widely used in data analysis and forecast data, Pattern Recognition bayes discriminant analysis algorithm.
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仿真效果非常好，可以实现模式识别领域的数据的分类及回归，用MATLAB编写的遗传算法路径规划。- Simulation of the effect is very good, You can achieve data classification and regression pattern recognition, Genetic algorithms using MATLAB path planning.
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具有丰富的参数选项,BP神经网络用于函数拟合与模式识别，计算两个矩阵之间的欧氏距离。- It has a wealth of parameter options, BP neural network function fitting and pattern recognition, Calculation of the Euclidean distance between the two matrices.
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包括最后计算压缩图像的峰值信噪比和压缩效果的源码,可以实现模式识别领域的数据的分类及回归，pwm整流器的建模仿真。- Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, You can achieve data classification and regression pattern recognition, Model
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计算时间和二维直方图,比较了软阈值，硬阈值及当今各种阈值计算方法,可以实现模式识别领域的数据的分类及回归。- Computing time and two-dimensional histogram, Comparison of soft threshold and hard threshold and today various threshold calculation method, You can achieve data classification and regression pa
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