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beyes
- C++写的一个贝叶斯分类算法,附有一个训练集数据和一个测试集数据-C++ write a Bayesian classification algorithm
cluster
- 聚类算法,对数据集进行聚类,得出训练后的胜出权值和所有权值-Clustering algorithm to cluster data sets, to win the right to come to training value and ownership of values
Support-vector-machine-model
- 支持向量机模型研究及应用,本文所做的工作主要在如下几个方面: 1)运用模糊集理论(FST)和概率理论对支持向量机进行研究,构造出了概率模糊支 持向量机(PFSVM)模型,既达到了减少或者消除外围异样点对于整个训练模型的影响,-Support vector machine model and application, this work mainly in the following aspects: 1) the use of fuzzy set theory (FST) and the
DCES
- 利用训练好的朴素bayesian网络对数据集进行分类。通过添加人工噪声的干扰来分类精度的检测。-By trained naive bayesian network to classify the data set. By the add artificial noise interference to the classification accuracy of detection.
pattern1_a
- . PCA人脸识别 A.闭集测试。用每个人的前5张图像作为训练,剩下的5张图像作为测试。也就是说总共有200张训练图像和200张测试图像。采用最近邻分类,分析选取不同的主分量个数K,对识别率的影响 -. PCA Face Recognition A. Closed set tests. With each of the first five images for training, the remaining 5 images as a test. That is a total of
A_Star
- A星算法解决八数码问题,代码共有两种,基础版用于接收外部输入,解决输入状态到输出状态的变化,训练版为在已写好的数据集(362880个数据)下测试运行效果,代码用C语言书写,较为简洁-A star algorithm to solve the problem of the digital code there are two basic version for receiving an external input, output changes state to resolve the inpu
KNN-Face-Recognition
- KNN分类算法实现人脸识别,数据集为ORL。训练样本分别为2、4、6,其余为测试样本。-KNN classification algorithm for face recognition, the data set for the ORL. 2,4,6 training samples respectively, the rest of the test samples.
FOA-ELM
- 算法思想是:1) 根据果蝇优化算法得到极速学习机隐层神经元的数目;2) 依据得到的隐层神经元数目和极限学习机的方法对训练样本和测试样本进行训练学习。只要打开fruitfly_elm.m文件运行即可,可以换数据集 -Algorithm idea is: 1) according to the number of flies speed machine learning algorithm to obtain the hidden layer neurons optimization Method
NB_for_text_classification
- 文本分类:朴素贝叶斯分类器例子,采用Multi-Variate Bernoulli Event Model。一个文件为训练,一个文件为测试,采用20newsgroups数据集。-Text classification: Naive Bayes classifier example, the use of Multi-Variate Bernoulli Event Model. A file for training, a file for testing, using 20newsgroups
myRBF_2
- 自己编写的RBF神经网络,采用的数据集是iris. 先使用KMeans找中心点,再训练RBF网络-I have written RBF neural network, using the data set is iris. First using KMeans find the center, retraining RBF network
feret
- feret人脸识别数据库,用于人脸分类训练,分为训练集合测试集-feret face recognition for human face classification training, divided into a training set of test set
Two-Variate-Function
- 使用BP神经网络实现二元函数的逼近问题,包含训练样本,无测试集-Using BP neural network to achieve the approximation of the two function function, including the training samples, no test set
deeplearing-hinton
- hinton2006年发表在science上的关于深度神经网络的文章Reducing the Dimensionality of Data with Neural Networks的matlab程序 mnistdeepauto.m //训练AutoEncoder的主文件 converter.m //将样本集从.ubyte格式转换成.ascii格式,然后继续转换成.mat格式 makebatches.m //创建小批量数据块用于RBM训练 rbm.m //训练RBM二进制隐层
INRIAHogLbpLabel
- 本方法是hog+lbp+svm来判断是否为行人,函数库为opencv,代码为C++,hog是opencv自带的,lbp为均匀模式,59维度,训练样本为INRIA数据集-opencv lbp svm president detection inria data
CNN
- 使用CNN卷积神经网络来训练MNIST数据集-CNN convolution using neural network training data set MNIST
MLP
- 使用MLP多层神经网络来训练MNIST数据集-Use MLP multi-layer neural network training data set MNIST
darknet
- 神经网络引入后,检测框架变得更快更准确。然而,大多数检测方法受限于少量物体。检测和训练数据上联合训练物体检测器,用有标签的检测图像来学习精确定位,同时用分类图像来增加词汇和鲁棒性。原YOLO系统上生成YOLOv2检测器;在ImageNet中超过9000类的数据和COCO的检测数据上,合并数据集和联合训练YOLO9-After the neural network is introduced, it is becoming faster and more accurate detection fr
MLkNN
- ML-KNN,这是来自传统的K-近邻(KNN)算法。详细地,为每一个看不见的实例中,首先确定了训练集中的k近邻。之后,基于从标签集获得的统计信息。这些相邻的实例,即属于每个可能类的相邻实例的数量,最大后验(MAP)原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究,即酵母基因功能分析、自然场景分类和网页自动分类,表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbo
AlexNet
- 使用TensorFlow 实现 AlexNet ,并使用 Mnist 数据集进行训练并测试。(AlexNet is implemented using TensorFlow and trained and tested using the Mnist data set.)
BP Matlab实现
- 采用Iris数据集在matlab上实现简单的数据训练和分类(Using Iris data set in matlab to achieve a simple data training and classification.)