资源列表
scalekmeans.tar
- Scalable k-means software and test datasets This package (a Unix tar file, gzipped) contains the source code for the software that was used to run the experiments for the article -Scalable k-means software and test datasets This package (a Unix tar f
EM
- 用C++实现了期望最大化算法，为了简化，矩阵使用对角矩阵-C achieve expectations maximize algorithm, in order to simplify the use of matrix diagonal matrix
winsvm
- 支持向量机分类器（可分类文本，编的非常不错）-The uploaded Support Vector Machine (SVM) Classifier can classify text-type data well.
Classifier4J
- Classifier4J这个Java类库为文本自动分类提供了一个API。缺省（目前）实现的API是一个贝叶斯分类器。这个类库可以用于多个目的－可能作为一个垃圾邮件过滤器或一个blog-Classifier4J Java class library for the automatic text classification provided an API. Default (current) to achieve the API is a Bayesian classifier. This lib
abooy
- hga是一种混合遗传算法c程序源代码，但是只包括了核心的算法部分，还没有完善。 gauss为电路试验中的高斯消去法。 GRKT10，Lagrange，Euler分别是数值计算中龙格－库塔法，拉个朗日法以及改进欧拉法的c程序实现 上述程序都是本人工作学习过程中自己编写的，本人只是一个程序新手，希望在本站能更好的学习-Unit is a mixed genetic algorithm c source code, however, covered only the core of the
NNBP1
- 人工神经网络bp算法的c语言的简单实现，有待进一步完善-artificial neural network algorithm bp c simple language to achieve, need to be further improved
esystem
- 简单的人工智能专家系统，包括了查询，添加知识等内容-simple artificial intelligence expert system, including a query, add content knowledge
k_algorithm
- K-均值算法的c语言实现，他是一种基于样本间相似性度量的间接聚类方法。-K-means is a VQ method based on the similarity of the samples. This is a C language source code for K-means algorithm.
复件 test1
- 程序是对心电信号进行噪声去除，采用的是数度较快的滤波器组，能快速完成计算任务-procedures of the ECG signal for noise removal, using a few degrees faster filters can rapidly complete computing tasks
复件 analbeat
- 程序是对心电信号进行R 峰检测，等电位线的检测等，能快速高效完成计算-procedures of the ECG R-detection, equipotential line testing can be completed quickly and efficiently computing
复件 classify
- 对心电数据进行处理，根据多标准的阈值法对心率失常进行判断和分类-right ECG data processing, multi-standard under the threshold value method to judge abnormal heart rate and classification
复件 match
- 心电数据的匹配，对数据中出现的情况分为三类进行匹配，正常，不正常，和未知，对不正常的数据进一步分析-ECG data matching, the data is divided into the three types of matches, normal, abnormal, and unknown to normal data for further analysis