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Description: 模式识别的主要工具集合，对于作模式识别的同行而言，是一个非常好的软件包！
希望大家多多补充，相互改进！-the main tool set for pattern recognition for the peer, it is a very good package! I hope Members can complement each other to improve!
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Size: 2726757 |
Author: 周副 |
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Description: matlab 模式识别工具包 希望能对你们有用
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Size: 352038 |
Author: Mr.zhang |
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Description: 模式识别工具包 有很多例子 适合做识别参考
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Size: 352193 |
Author: li shu |
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Description: 模式识别工具包，有很多模式识别的算法，可以在matlab中调用
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Size: 354375 |
Author: liujingwei |
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Description: 模式识别工具包
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Size: 357294 |
Author: kxgwan |
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Description: matlab 模式识别工具包 希望能对你们有用-pattern recognition matlab toolkit hope to be useful to you
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Size: 352256 |
Author: Mr.zhang |
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Description: 模式识别工具包 有很多例子 适合做识别参考-Pattern recognition tool kit has a lot to do to identify examples of suitable reference
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Size: 352256 |
Author: li shu |
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Description: 模式识别工具包，有很多模式识别的算法，可以在matlab中调用-Pattern recognition tool kit, there are many pattern recognition algorithms, you can call matlab
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Size: 354304 |
Author: liujingwei |
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Description: 模式识别工具包，matlab编写，非常好用哦。-Pattern Recognition Toolkit, matlab prepared, very easy to use, oh.
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Size: 410624 |
Author: 束鑫 |
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Description: 统计模式识别工具包(统计模式识别工具包，包含EM，SVM等方法。)-Statistical Pattern Recognition Kit (statistical pattern recognition tool kit, including EM, SVM and other methods.)
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Size: 4090880 |
Author: lyli |
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Description: 模式识别matlab工具包，包含常见的模式识别方法的源程序-Pattern recognition matlab toolkit, including a common source of pattern recognition methods
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Size: 342016 |
Author: bocai |
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Description: 基于统计的模式识别工具包The core of the STPRtool is comprised of statistical PR algorithms described in
the monograph Schlesinger, M.I., Hlav′aˇc, V: Ten Lectures on Statistical and Structural
Pattern Recognition, Kluwer Academic Publishers, 2002-Statistical Pattern Recognition Toolbox for Matlab
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Size: 6004736 |
Author: newfacer |
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Description: ：人工神经网络中的BP网络模型在函数逼近、模式识别等领域得到了广泛的应用，但利用神经网络
解决实际问题时，经常涉及到大量的数值计算问题，而运用计算机高级语言编程对神经网络BP模型进行仿真和
辅助设计是件十分麻烦的事情，为了解决这个矛盾，Matlab工具箱中专fq编制了大量有关设计BP网络模型的工
具函数 本文分析了Matlab软件包中人工神经网络工具箱的有关BP网络的工具函数，并给出了部分重要工具函
数的实际应用．-: Artificial Neural Networks in the BP network model in function approximation, pattern recognition and other fields a wide range of applications, but the use of neural networks to solve practical problems, often involving a large number of numerical problems, the use of high-level computer programming language to the neural network BP model is a piece-aided design simulation and a very troublesome thing, in order to solve this contradiction, Matlab toolbox secondary fq compiled a large number of BP network model of the design of utility functions This paper analyzes the Matlab software package on artificial neural network toolbox BP network, utility functions, and gives some important tools for the practical application of the function.
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Size: 118784 |
Author: 杨元龙 |
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Description: 模式识别的工具包，开发语言是matlab，里面含有常用的实验数据集，是学习模式识别的入门实验。-prtools for pattern recognition
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Size: 335872 |
Author: shiwy |
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Description: stprtool的使用文档，非常好的模式识别工具包，里面有关于核的以及贝叶斯的相关程序，以及PCA，EM，SVM等方法。 -stprtool the use of the document, very good pattern recognition tool kit, which has nuclear as well as Bayes procedures, as well as PCA, EM, SVM and other methods.
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Size: 923648 |
Author: wangpw |
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Description: 模式分类工具包，内含大量的模式识别算法。可直接放于toolbox文件夹下，建立路径即可。-Pattern classification tool kit, containing a large number of pattern recognition algorithms. Can be put in the toolbox folder directly under the established path.
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Size: 1253376 |
Author: 华仔 |
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Description: MATLAB 模式识别包 初学者的好工具 -Pattern recognition package MATLAB good tool for beginners
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Size: 638976 |
Author: 王慧 |
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Description: 模式识别工具包,数学建模的好工具，大家一定会用的到的。-model use of math how to make masure and like it,will you ？
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Size: 1024 |
Author: 阿飞 |
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Description: 是一个模式识别工具包，里面包含bayes,linear分类函数，还有支持向量机SVM工具包-It is a pattern recognition toolbox, which contains the bayes, linear classification function, as well as support vector machines SVM toolbox
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Size: 5577728 |
Author: 刘风华 |
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Description: 机器学习和模式识别工具包spider。内容很丰富。包含svm 决策树（C45,J48）、svm、knn、adaboost、bagging、hmm（隐马尔科夫模型）、随机树（random forest）等-Machine learning and pattern recognition toolkit spider. Very rich in contents. Tree contains svm (C45, J48), svm, knn, adaboost, bagging, hmm (hidden Markov model), random trees (random forest), etc.
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