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train-images-idx3-ubyte
- MNIST数据集中图像数据文件， 60000个训练集-The MNIST dataset image data files, 60000 training set
KNN
- Implement the K nearest neighbor algorithm by your own instead of using available software. 2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result. 3. T
svmTrain
- [model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an SVM classifier and returns trained model. X is the matrix of training examples. Each row is a training example, and the jth column holds the jth feature. Y is a column
mlclass-ex3
- 多分类学习及神经网络，机器学习相关，基于matlab计算-ex3.m- Octave scr ipt that will help step you through part 1 ex3 nn.m- Octave scr ipt that will help step you through part 2 ex3data1.mat- Training set of hand-written digits ex3weights.mat- Initial weights for the
mlclass-ex4
- 神经网络学习，基于matlab仿真，机器学习相关知识-ex4.m- Octave scr ipt that will help step you through the exercise ex4data1.mat- Training set of hand-written digits ex4weights.mat- Neural network parameters for exercise 4 submit.m- Submission scr ipt that sends you
mlclass-ex6
- 支持向量机，实现2或多分类，基于matlab仿真，内有说明-ex6.m- Octave scr ipt for the rst half of the exercise ex6data1.mat- Example Dataset 1 ex6data2.mat- Example Dataset 2 ex6data3.mat- Example Dataset 3 svmTrain.m- SVM rraining function svmPredict.m- SVM p
charSamples
- 车牌识别的训练字符库，包括10个数字，24个英文字符(License plate recognition training character library, including 10 numbers, 24 English characters)
CCKS2017
- CCKS2017(China Conference on Knowledge Graph and Semantic Computing)的电子病历数据，可以用于命名实体识别的训练(CCKS2017(China Conference on Knowledge Graph and Semantic Computing) dataset, can be used for NER system training)
GeoDetector_2015_Example(Toy Dataset)D3
- 简易处理空间异质性问题。需要做好分层准备。(Simple training network can deal with spatial heterogeneity easily. We need to be well prepared.)
fenlei
- 利用深度学习进行遥感图像场景分类 这里我们对NWPU-RESISC45数据集的场景图像进行分类 我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning Here we classify scene images from NWPU-RESISC45 dataset We apply
L4_1
- a）产生两个都具有200个二维向量的数据集和（注意：在生成数据集之前最好使用命令randn(‘seed’,0)初始化高斯随机生成器为0（或任意给定数值），这对结果的可重复性很重要）。向量的前半部分来自均值向量的正态分布，并且协方差矩阵。向量的后半部分来自均值向量的正态分布，并且协方差矩阵。其中是一个2*2的单位矩阵。 （b）在上述数据集上和分别属于+1类和-1类，请在上述数据集的两类中各随机抽取150个样本作为训练集，运用Logistic regression算法得到的分类面，然后对余下的各5