搜索资源列表
RBF
- RBF神经网络应用数据预测，有训练集，测试集(Application of BF neural network data forecas)
knn
- k最近邻算法:分类和回归。通过对训练集分类训练模型，验证集用于验证数据的准确性。(K nearest neighbor algorithm: classification and regression. Through the training set classification training model, the verification set is used to verify the accuracy of the data.)
naive_bayes
- 朴素贝叶斯算法分类及回归，附带训练集和测试集，可以评测正确率和输出预测文件(Classification and regression of naive Bayes algorithm, incidental training set and test set can evaluate the correct rate and output prediction file.)
homework2_2
- 实现批处理感知器算法的程序，用于分类训练集，同时记下收敛时的步数(Program to realize batch perceptron algorithm for classification of training set, and steps to write down the convergence)
trainvaltxtproduct
- 创建训练和验证集。在数据集中，使用脚本语言自动创建(Creating training and validation sets)
svmtrain
- 基于支持向量机的对指定多个包含特征的训练集图片，包含label信息。训练后，可对于相同格式的图片进行分类。(A training set image containing multiple features is included in the support vector machine (SVM), which contains label information. After training, the pictures in the same format can be classifi
SVM
- 训练集：trainset(); 分别取bedroom(1:5,:)和forse(1:5,:)作为训练集； 测试集：testset(); 分别取bedroom(6:10,:)和forse(6:10,:)作为测试集； 标签集：label(); 取bedroom的数据为正类标签为1；forse的数据为负类标签为-1.(Training set: trainset (); take bedroom (1:5,) and forse (1:5,:) as the training set; Tes
BPNN
- 能够实现bP算法，数据集使用的是iris里面的花卉相关的数据，训练集是0.7，测试集是0.3.(BP algorithm can be realized)
a01
- 利用ML算法对训练集进行学习，利用多维高斯进行判断后对输入图片进行前景后景判断(The training set for learning to use ML algorithm to judge on the input image foreground & background)
大数据_协同过滤_梯度下降
- 给定10000个用户和他们对10000个电影的评价，然后通过协同过滤或梯度下降算法，用训练集训练数据，预测出用户对未看的电影的评分，并与测试集对比验证预测结果的准确性(You can learn Chinese,and read the Chinese introduction.)
神经网络
- 单隐藏层神经网络，五折交叉验证外加训练集(Single hidden layer neural network)
Linear Regression
- 线性回归实现人脸识别，有40类人脸图片共400张，200个训练集，200个测试集，通过2折交叉验证得到准确率为89%(Face recognition by linear regression)
基于BP神经网络的手写数字识别
- 这是一份基于BP神经网络的手写数字识别文件，包含了源代码、最优权重向量、以及带标记的训练集。代码中给出了详细的注释，对于理解神经网络的原理有很好的帮助。
SVM_VER_3.0
- SVM算法模型，可根据具体的训练集训练自己的算法模型。(SVM algorithm model and method, you could check the source code for matlab and study it.)
elmshixian(jiazhushi )
- 利用elm算法进行多分类或者回归，实现训练集和数据集的处理和分类(Using the elm algorithm for multiple classification or regression,realize the processing and classification of training set and data set)
tist_aux__statement
- 支持向量机,用于分类,含训练集与测试集,()
hadio__command
- 支持向量机,用于分类,含训练集与测试集,里面含有六个源程序()
情感分析
- 分类，用于对情感词的统计、排序、分类等 包括源程序、数据拆分程序、训练集和测试集(Classification for statistics, sorting and classification of emotional words Includes source programs, data split programs, training sets, and test sets)
Recognition
- 将数量较少的故障样本分为训练集和测试集，实现故障的分类和识别(A small number of fault samples are divided into training set and test set to realize fault classification and recognition.)
KNN分类器
- 一、用python或matlab编写一个KNN分类器 训练集为semeion_train.csv（手写体识别） 测试集为semeion_test.csv 计算在测试集上错误率（k=1,k=3,k=5,k=10） ?(1. Write a KNN classifier with Python or matlab Training set is semeion_train.csv (handwriting recognition) The test set is semeion_test