搜索资源列表
PG_BOW_DEMO-master
- 图像检索算法 BOW 有机器学习的部分 分为训练集和测试集 效果不错 不懂的可以阅读里面的readme.txt 文件
pmf
- 推荐系统 概率矩阵分解代码 内部已包含有数据集,已分为训练集与测试集-Recommended system probability matrix decomposition Code
self-taught-learning
- 自主学习把稀疏自编码器和分类器实现结合。先通过稀疏自编码对无标签的5-9的手写体进行训练得到最优参数,然后通过前向传播,得到训练集和测试集的特征,通过0-4有标签训练集训练出softmax模型,然后输入测试集到分类模型实现分类。-Independent Learning the encoder and the sparse classifiers achieve the combination. First through sparse coding since no label was ha
kennard_stone
- Kennard Stone 算法 用于数据集的划分(训练集 和 测试集) 算法同时输出训练集、测试集,以及训练集或测试集中样品在原数据集中的编号信息,方便样本的查找。 原始代码来源于本网,自行重新编译,如有需要,欢迎下载。 -Kennard Stone agorithm for the partition of data set (training set and test set) the outputs of the agorithm not only include
plot_isotonic_regression
- 保序回归是寻找使训练集均方差最小的近似函数,它的优点是目标函数不要线性的。-The isotonic regression finds a non-decreasing approximation of a function while minimizing the mean squared error on the training data. The benefit of such a model is that it does not assume any form for the tar
BP_Classifier
- 用MATLAB实现的简单分类器,算法为BP神经网络,为监督学习,需要训练集(文件中附有训练集,供测试用),分类效果较好。-This program creates a Classifier to identify the gender by height and weight based on BP network.
SVM-class
- 这是关于svm的java源代码,带训练集,和测试集-This is about svm java source code, with training set and test set
fisher_classify
- MATLAB版本的LDA线性分类器,具体包括计算类内离散度矩阵,类间离散度矩阵,以及训练集各类在新坐标轴上的投影。代码原来用于肌电特征的分类,亦可用于其他机器学习案例-the LDA classifier wrote in MATLAB
NN_tutorial
- 基于NN网络的图像训练及分类,程序中包含输入图像矩阵,训练后测试具有100 正确率,可以自己加入不同的训练集,直接运行即可-NN network-based image classification training and procedures contained in the input image matrix test after training with 100 accuracy, can add their own different training set can be ru
BPtrain
- BP神经网络实现测试数据预测(将训练集与测试集数据进行归一化 建立BP神经网络,并训练;利用训练好的BP神经网络对测试集中的23个样本的抗压强度进行预测;输出结果并绘图)-BP neural network to predict the test data (the training set and test data set is normalized the BP neural network and training use of the trained BP neural netwo
GRNN_PNN
- 将训练集与测试集数据进行归一化; 建立GRNN或PNN神经网络; 利用建立好的神经网络对测试集中的26个乳腺组织样本的类型进行预测; 计算预测正确率(不必计算每类的正确率,只需计算正常或者病变两类的正确率,即只要预测结果与真实值属于同一大类,则认为是正确,否则认为预测错误)-The training set and test data set is normalized Establish GRNN or PNN neural network The use of wel
elmtrain
- 将整个数据集中的103个样本随机划分为训练集与测试集,其中训练集包含80个样本,测 试集包含23个样本; 建立极限学习机模型,并训练; 利用训练好的极限学习机模型对测试集中的23个样本进行预测; 输出结果并绘图(真实值与预测值对比图); -The 103 random samples of the entire data set is divided into training set and test set, wherein the training s
biaoqingshibie
- 是对jaffe人脸库进行识别测试的主程序,将jaffe人脸库分为训练集和测试集两部分,首先对图片进行LBP+LPQ特征提取,然后svm分类识别,统计识别率 -Is jaffe face recognition test the main library, the library will jaffe face divided into training and test sets of two parts, the first of LBP+LPQ image feature extrac
bayes
- 首先对数据进行拆分,分为测试集与训练集,通过训练集进行贝叶斯网络的建模,最后利用建立的模型进行预测或分类任务的R语言代码-First, the data is split into a training set and test set, Bayesian network modeling through the training set, and finally the use of the model to predict or classify tasks R language code
NLPLibSVM
- libsvm分词训练集的java版本。包括libsvm.jar以及训练集样本-Libsvm version of the Java word segmentation training set. Including libsvm.jar and training set samples
demoadaboost
- Adaboost是一种迭代算法,其核心思想是针对同一个训练集训练不同的分类器(弱分类器),然后把这些弱分类器集合起来,构成一个更强的最终分类器(强分类器)。-Adaboost is an iterative algorithm, the core idea is the same for a training set different classifiers (weak classifiers), and then set up these weak classifiers to form a
k_nn
- kNN的思想:计算待分类的数据点与训练集所有样本点,取距离最近的k个样本;统计这k个样本的类别数量;根据多数表决方案,取数量最多的那一类作为待测样本的类别。距离度量可采用Euclidean distance,Manhattan distance和cosine。-kNN The idea is simple: the training set and calculated data points to be classified all sample points taken the neare
email
- 机器学习算法的数据集,包含训练集和测试集。主要用于邮件分类-Machine learning algorithms of data sets, including training set and testing set.Mainly used for E-mail classification
444
- 算法流程:选定训练集和测试集-数据预处理-交叉验证选择最佳参数-分类准确率-预测-利用最佳参数训练SVM-Algorithm flow: selected training set and test set- data preprocessing- cross-validation selection of the best parameters- classification accuracy- prediction- training SVM using the best parameter
biaoqing
- 对jaffe人脸库进行识别测试的主程序,将jaffe人脸库分为训练集和测试集两部分,首先对图片进行LBP+LPQ特征提取,然后svm分类识别,统计识别率-Jaffe face for the identification of the main test will jaffe face is divided into a training set and a test set of two parts, the first of LBP+LPQ image feature extractio