搜索资源列表
newpnn
- 基于GMM的概率神经网络PNN具有良好的泛化能力,快速的学习能力,易于在线更新,并具有统计学的贝叶斯估计理论基础,已成为一种解决像说话人识别、文字识别、医疗图像识别、卫星云图识别等许多实际困难分类问题的很有效的工具。而且PNN不但具有GMM的大部分优点,还具有许多GMM没有的优点,如强鲁棒性,需要更少的训练语料,可以和其他网络其他理论无缝整合等。-GMM based probabilistic neural network PNN good generalization ability, the
FullBNT
- 贝叶斯网络matlab源程序,可用于分类,欢迎大家下载测试-Bayesian network Matlab source, used to classify and you are welcome to download test!
TextCategorization
- 基于朴素贝叶斯算法实现的中文文本分类程序。可以对中文文本进行分类识别,使用时先对分类器进行训练,然后进行识别。该Beta版本仅支持对3类文本进行分类,使用简单的中文分词方法,本程序尚不具备实用性,用于算法研究和改进。-based on Bayesian algorithms to achieve the Chinese text classification procedure. Can the Chinese text classification identification, the us
pattern-recognize
- 模式识别分类程序,贝叶斯,神经网络分类训练程序,很不错啊-the classification procedures, Bayesian neural network classifier training procedures, and it is very responds :
JavaBayes_src_v.0.346.2
- java编写的贝叶斯网络分类器(貌似没有模型构建和参数学习过程)-java prepared by the Bayesian network classifier (seemingly no model of the learning process and parameters)
jBNC_src_v.1.2.2
- bayes network classifier toolbox 贝叶斯网络分类工具箱-bayes network toolbox Bayesian classifier network classifier Toolbox
jBNC_bin_v.1.2.2
- bayes network classifier toolbox 贝叶斯网络分类器工具箱的bin文件-bayes network toolbox Bayesian classifier network classifier Toolbox for the paper bin
bayes1111
- Bayes分类器设计,对模式识别有一个初步的理解,能够根据自己的设计对贝叶斯决策理论算法有一个深刻地认识-Bayesian classifier design, pattern recognition to a preliminary understanding, According to the design of Bayesian decision theory algorithm has a profound understanding
KNN(CSHARP)
- 基于不断学习的贝叶斯-KNN文本分类算法的设计与实现,给出原始几个类别的文本文件,通过机器学习,获取各个类别文本内容的主要特征,在这个基础上,给出待分类的文件库,系统通过自动分类,对文件库中的文本进行分类,把文件分配到最有可能的类别中。-based learning Bayesian-KNN text classification algorithm design and implementation given several types of the original text file,
pattern-recongnation
- 该程序设计采用了模式识别中的多种方法: 模板匹配§贝叶斯¥几何分类器×神经网络法等分类方法
Di
- 贝叶斯bayes算法分类器诊断程序-Bayesian classifier diagnostic procedures
MINST_DEMO
- 根据概率模型实现的QDF,LDF分类算法,有minst数据集做demo(QDF, LDF classification algorithm based on probability model, MINST data set to do demo)
bayes_C++
- 贝叶斯分类器-联合变量_C++,只需更改样本文件名即可测试。(The Bias classifier - the joint variable _C++, can be tested only by changing the name of the sample file.)
bayes_independent variable _C++
- 贝叶斯分类器-独立变量_C++,只需更改样本文件名即可测试。(Bias classifier - independent variable _C++, can be tested only by changing the name of the sample file.)
bayes_independent variable _matlab
- 贝叶斯分类器-独立变量_matlab,只需更改样本文件名即可测试。(Bias classifier - independent variable _matlab, can be tested only by changing the name of the sample file.)
bayes_joint variable _matlab
- 贝叶斯分类器-联合变量_C++,只需更改样本文件名即可测试。(The Bias classifier - the joint variable _C++, can be tested only by changing the name of the sample file.)
74554399
- 朴素贝叶斯分类算法,可以用来进行分类bayes()
Classifiers
- 我们需要成百上千的分类器来解决现实世界的分类吗 我们评估179分类17种分类器(判别分析,贝叶斯,神经网络,支持向量机,决策树,基于规则的分类器,升压、装袋、堆放、随机森林和其他合奏,广义线性模型,线性,偏最小二乘法和主成分回归,logistic回归、多项式回归、多元自适应回归样条等方法),实现在WEKA,R(有或没有插入包),C和Matlab,包括所有目前可用的相关分类。(Do-we-Need-Hundreds-of-Classifiers-to-Solve-Real-World-Class
python_self
- 实现了机器学习的各种分类算法,如:knn,svm,朴素贝叶斯,神经网络,决策树等。(Various classification algorithms of machine learning, KNN, SVM, naive bayes, neural network, decision tree, etc.)
Pattern Recognition
- matlab实现一些基础的模式识别工作,如贝叶斯分类,聚类算法,bp神经网络(Matlab implements some basic pattern recognition work, such as Bayesian classification, clustering algorithm, BP neural network)