搜索资源列表
AGAandLAGA
- 两种改进的遗传算法（自适应交叉概率的遗传算法，加入领域竞争策略的遗传算法）相比较的matlab程序，基于UCI的两个数据集，可直接运行程序观察效果。-both improved genetic algorithm (adaptive crossover probability of genetic algorithm, to field a competitive strategy of genetic algorithm) compared to the Matlab procedures,
mat
- 用于人工智能分类算法仿真的数据集 UCI数据集 已经过集成-Simulation of artificial intelligence classification algorithm for data sets UCI data sets have been integrated
fuzzyPR
- 模糊聚类的经典算法，可以使用UCI数据集进行聚类，该程序附有详细的说明-Classical fuzzy clustering algorithm, you can use the UCI data sets clustering, the program is accompanied by a detailed descr iption of
wine
- pca-kmeans聚类 先将数据(wine,uci数据集)降维处理，在进行聚类-pca-kmeans clustering use the data of uci:wine.
dat_banana
- 香蕉形(banana)标准数据集，用于测试机器学习与模式识别算法。-Banana-shaped standard data set for testing machine learning and pattern recognition algorithm.
NB
- 自己编写的NB函数，可以读取UCI数据集中任意一个数据集。-I have written the NB function, can read any of the UCI data set data set.
NB_breast_cancer_wisconsin_original
- 基于朴素贝叶斯的分类练习，在UCI数据库中的breast数据集上进行的测试-Bayesian classification based on practice, in the UCI database data set on breast test
LVQandSOM
- perform SOM and LVQ on 2 UCI dataset and compare thier classification accuracy.
KNN4hypothroid
- this source code claculate KNN for hypothroid uci dataset
SMOSVM
- 用于svc分类， 用SMO实现SVC，并在UCI数据集Iris上进行实验,2. 可借鉴现成SVM软件包，实现回归分析。 -For svc classification, using SMO to achieve SVC, and in the UCI Iris data set on the experiment, 2. SVM can learn ready-made packages, to achieve regression analysis.
SpokenArabicDigit
- this matlab code reads the data text files UCI Spoken Arabic Digits and save the results in a mat file which user can easily use.
wine
- UCI红酒分类，matlab码源，已调试-Liquor classification pattern recognition
Matlab_SVM
- SVM算法实现+数据 (要用到一些包，按照代码里面的import到网站下就行） 1.读取数据：在Matlab中调用textread可读取UCI数据集，这里读取的文件是iris.data，因为文件中以逗号为分隔符，所以还要在读取方法中添加参数“‘delimiter’，‘，’”，从而在读数据的时候自动跳过分隔符。 2.调用cvx工具箱中的方法：首先需要下载cvx工具箱的压缩文件，在其目录下运行cvx_setup指令，然后调用其方法，以cvx_begin开头，cvx_end为终止符号，所有需
UCI
- 用于神经网络训练的UCI数据集，mat格式(UCI training dataset for ANN training with mat form)