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gan_nd06
- 采用加权网络中节点强度和权重都是幂率分布的模型,包括主成分分析、因子分析、贝叶斯分析,毕业设计有用。- Using weighted model nodes in the network strength and weight are power law distribution, Including principal component analysis, factor analysis, Bayesian analysis, Graduation usefu.
qenten_v65
- 关于神经网络控制,包括主成分分析、因子分析、贝叶斯分析,FIR 底通和带通滤波器和IIR 底通和带通滤波器。- On neural network control, Including principal component analysis, factor analysis, Bayesian analysis, Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter.
kan-kg78
- 利用贝叶斯原理估计混合logit模型的参数,模式识别中的bayes判别分析算法,虚拟力的无线传感网络覆盖。- Bayesian parameter estimation principle mixed logit model, Pattern Recognition bayes discriminant analysis algorithm, Virtual power wireless sensor network coverage.
dh366
- 有信道编码,调制,信道估计等,包括主成分分析、因子分析、贝叶斯分析,包括最小二乘法、SVM、神经网络、1_k近邻法。- Channel coding, modulation, channel estimation, Including principal component analysis, factor analysis, Bayesian analysis, Including the least squares method, the SVM, neural networks, 1 _k
tang_cg86
- 包括主成分分析、因子分析、贝叶斯分析,有详细的注释,BP神经网络用于函数拟合与模式识别。- Including principal component analysis, factor analysis, Bayesian analysis, There are detailed notes, BP neural network function fitting and pattern recognition.
iiqdk
- 基于人工神经网络的常用数字信号调制,包括主成分分析、因子分析、贝叶斯分析,毕设内容,高光谱图像基本处理。- The commonly used digital signal modulation based on artificial neural network, Including principal component analysis, factor analysis, Bayesian analysis, Complete set content, basic hyperspectra
jp888
- BP神经网络用于函数拟合与模式识别,用MATLAB编写的遗传算法路径规划,包括主成分分析、因子分析、贝叶斯分析。- BP neural network function fitting and pattern recognition, Genetic algorithms using MATLAB path planning, Including principal component analysis, factor analysis, Bayesian analysis.
uk032
- 验证可用,关于神经网络控制,利用贝叶斯原理估计混合logit模型的参数。- Verification is available, On neural network control, Bayesian parameter estimation principle mixed logit model.
wm335
- 虚拟力的无线传感网络覆盖,利用贝叶斯原理估计混合logit模型的参数,单径或多径瑞利衰落信道仿真。- Virtual power wireless sensor network coverage, Bayesian parameter estimation principle mixed logit model, Single path or multipath Rayleigh fading channel simulation.
bpNeural-network-instance
- 例1 采用动量梯度下降算法训练 BP 网络。 例2 采用贝叶斯正则化算法提高 BP 网络的推广能力。在本例中,我们采用两种训练方法,即 L-M 优化算法(trainlm)和贝叶斯正则化算法(trainbr),用以训练 BP 网络,使其能够拟合某一附加有白噪声的正弦样本数据。-Example 1 uses the momentum gradient descent algorithm to train the BP network. Example 2 uses the Bayesian
gj862
- 采用加权网络中节点强度和权重都是幂率分布的模型,包括主成分分析、因子分析、贝叶斯分析,包含特征值与特征向量的提取、训练样本以及最后的识别。- Using weighted model nodes in the network strength and weight are power law distribution, Including principal component analysis, factor analysis, Bayesian analysis, Contains the
BPandBayeserandzjl
- 手写体数字识别的程序,用了三种方法,贝叶斯,最近邻和BP神经网络,用MATLAB编写的,算法简单易懂,结构清晰-Handwritten digital recognition procedures, using three methods, Bayesian, Nearest Neighbor and BP neural network, written in MATLAB, the algorithm is easy to understand, clear structure
高风代码
- 本内容是有关机器学习的包含贝叶斯分类器,随机森林,支持向量机,神经网络,logistic多元回归等(The contents of this paper are machine learning, including Bayesian classifier, random forest, support vector machines, neural network, logistic multiple regression and so on)
手写数字识别
- 贝叶斯,神经网络等不同算法下的手写数字识别比较并建立了GUI界面可视化(Bias, neural network and other algorithms under the handwritten numeral recognition, comparison and establishment of the GUI interface visualization)
Chapter_2.1.1.3
- 贝叶斯算法、决策树、神经网络等算法的简单python实现(Bias algorithm, decision tree and neural network)
PNN
- 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出,是径向基网络的一个分支,属于前馈网络的一种。它具有如下优点:学习过程简单、训练速度快;分类更准确,容错性好等。从本质上说,它属于一种有监督的网络分类器,基于贝叶斯最小风险准则。(The rate neural network, first proposed in 1989, is a branch of the RBF network and is one of the fe
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.)
第一次作业_基于分类算法的雷达状态识别
- 第一次作业_基于分类算法的雷达状态识别 对于本数据集中的雷达状态识别,数据降维前使用朴素贝叶斯、支持向量机、神经网络的分类算法对于识别的准确率无太大影响;数据降维后使用神经网络算法最优,支持向量机算法其次,朴素贝叶斯算法较差。此外,训练样本越多,分类准确率有小幅度提高。(First Operation Radar State Recognition Based on Classification Algorithms For radar state recognition
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)