搜索资源列表
Naive_Bayesian_classify_version
- 朴素贝耶稣算法进行文本分类，删除“无用词”，对训练集训练之后完成对测试集的测试，并输出测试集文档属于哪个分类-Tony simple algorithm for text classification Jesus, delete " without words" , after training set for the completion of the test set of tests and test sets the output document belongs Cat
Adaboost
- 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-Adaboost is an iterative algorithm, the core idea is the same training set for different classifiers (weak classifiers), and then set up these weak classifiers to form a stronger
Adaboost
- 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-Adaboost is an iterative algorithm, the core idea is the same training set for different classifiers (weak classifiers), and then set up these weak classifiers to form a stronger
CNN
- 用 卷积神经网络进行手写字符 识别,内含mnist训练集-Handwritten character recognition, containing mnist convolution neural network training set
4.2
- 用人工神经网络拟合函数 说明：1）网络结构为三层（输入层、1个隐层和输出层） 2）获取两组数据，一组作为训练集，一组作为测试集 3）用训练集训练网络 4）用测试集检验训练结果-ANN fit function: 1) the network structure is three (input layer, a hidden layer and output layer) 2) to obtain two sets of data, one group as the
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
ID3
- ID3算法的C++实现，实现通过训练集建立决策树，测试集可以测试决策树的准确性-the realize of ID3 algorithm by c++
character-training-set
- 车牌识别，各省汉字训练集，全是手动筛选的，部分省市素材缺少所以缺乏样本-License plate recognition, Chinese character training set
TextonBoostSplits
- Textonboost用boosting实现基于纹理特征的图像分类，里面有训练集、测试集和验证集，具有一定参考价值。-Textonboost uses boosting to realize image classification based on texture features, which has training set, test set and validation set, which has a certain reference value.
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.
syn_13
- 以网格采样方法构建训练集,训练决策树，对图像分类。-Grid sampling method for constructing the training set, training the decision tree, for image classification.
syn10_1
- 以多边形采样结果构建训练集,对图像进行分类-Polygon sampling results build the training set, the image classification
ColorIndex
- 在Corel 5k数据库中，首先提取训练集和测试集中所有图像的直方图信息（AllHist.m），然后利用直方图相交法检索图像（ColorIndex.m）。-Firstly, read all images, and get their hists, then retrieve image by color index.
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