搜索资源列表
zuoye1
- 用FEMALE.TXT和MALE.TXT的数据作为训练样本集,建立Bayes分类器,用测试样本数据对该分类器进行测试。-With the older MALE. TXT. TXT and data as the training sample set, establish Bayes classifier, test sample data with the classifier for testing.
MARK_KNN
- k-nearest-neighbor K近邻算法的C++实现,训练后模型可以对测试数据进行归类。找了一些PUDN上的程序发现都没有很好的训练和测试数据集,所以自己写了一个直接放在main函数里面,不要从外部文件读取。工程运行于vc++ 6.0环境。Debug目下exe文件可以直接双击运行查看结果。-K nearest neighbor of C++ implementation, training model can be classified on the test data. Find so
xiangsidu
- 相似度算法的实现, 在利用支持向量机进行模式分类的时候,有时需要考虑到实时性,为了提高实时性,则利用相似度算法减少样本集个数,从而减少训练时间和支持向量的个数,使得建立起的向量机实时性提高。-Similarity algorithm, support vector machine in pattern classification, it is sometimes need to take into account the real-time, in order to improve real-
CLASSIFY
- matlab数学建模工具箱,线性统计聚类 (样品集,训练品集,训练品类别集)将各样品分类。-matlab toolbox of mathematical modeling, linear statistical clustering (Sample set, set of training materials, training, product category set) to the sample classification.
Machines-Based-on-DFS
- 深度优先搜索的支持向量机参数优化算法 Study on Parameters Optimization of Support Vector Machines Based on DFS :研究支持向量机参数优化问题,由于算法要求准确选择 SVM 参数,支持向量机在处理大样本数据集时和最优模型参 数确定时,消耗的时间长、占有内存大,易获得局部最优解的难题。为了解决支持向量机存在的不足,采用深度优先搜索算 法对其参数优化机机制进行改进。将向量机参数优化视成一个组合优化问题,将支持向
SVMv1
- 支持向量机C-SVC分类算法,构造两类训练数据集-Support Vector Machine C-SVC algorithmConstruct two types of training data set,
Aempcaal
- EMPCA算法的函数代码,附带有训练测试数据集,用用于特征降维等方面。 ,经测试可直接使用。 -The function code of the EMPCA algorithm with the training set of test data, for feature dimensionality reduction and other aspects. Has been tested and can be used directly.
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
system_s-function(mimo)
- 这是用SIMULINK的S-FUNCTION方式编写的系统仿真代码,系统是基于4G网络的核心技术空时分组编码的MIMO-OFDM通信系统的,涉及到空间分集、时间分集和频率分集的有机结合,包括QPSK调制解调、IFFT调制、控制编解码、基于训练符号的信道估计等通信模块。包括main_STBC_MIMO_OFDM和training_symbol两个M文件和一个DOC文件,对各部分的都有详细的代码和注释。-It is used SIMULINK the S-FUNCTION approach to t
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
Intrusion_detection
- 决策树算法,从mysql数据库中读取数据训练,KDD CUP 99的数据集,预测网络入侵。成功率在85 左右-The decision tree algorithm, get data from mysql for training KDD CUP 99 data sets, predict network intrusion. The success rate is about 85
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.
flower
- 对花卉的数据集分别通过“五折法”、随机产生训练样本、欧式平方距离、绝对值距离、契比雪夫距离和马氏距离进行数据集的识别。-Data sets, respectively, for flowers through the " half of Law" , randomly generated training samples, European squared distance, absolute distance, Chebyshev distance and Mahalanobi
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
PCA
- PCA算法实现对数据降维,train_sample为训练样本,train_class为训练样本的分类结果,test_sample为测试样本,test_class为测试样本的分类结果,可以从UCI下载数据集进行调用~-PCA algorithm for data dimensionality reduction, train_sample of training samples, train_class for the classification of training samples, tes
UnionSet
- POJ1703 引自余立功《算法训练教程》,并查集的应用-POJ1703 union set