资源列表
chapter13
- LIBSVM参数实例详解 -Detailed examples LIBSVM parameters
chapter14
- 基于SVM的数据分类预测——意大利葡萄酒种类识别-Data classification based on SVM prediction- Italian wine species identification
PSOSA5
- 基于模拟退火的粒子群优化算法，算法的迭代速度快，精度高- particle swarm optimization with simulated annealing
chapter15
- SVM的参数优化——如何更好的提升分类器的性能-SVM parameter optimization- how to better enhance the performance of the classifier
chapter16
- 基于SVM的回归预测分析——上证指数开盘指数预测.-SVM regression analysis to predict based on- the Shanghai Composite Index opened Index Forecast.
chapter17
- 基于SVM的信息粒化时序回归预测——上证指数开盘指数变化趋势和变化空间预测-SVM regression prediction based on information granulation timing- the Shanghai Composite Index opened index trends and changes in spatial prediction
chapter18
- 基于SVM的图像分割-真彩色图像分割-SVM-based image segmentation- true color image segmentation
chapter19
- 基于SVM的手写字体识别 -SVM-based handwriting recognition
chapter20
- LIBSVM-FarutoUltimate工具箱及GUI版本介绍与使用-LIBSVM-FarutoUltimate toolbox and GUI versions introduction and use
dbscan
- DBSCAN(Density-Based Spatial Clustering of Applications with Noise)是一个比较有代表性的基于密度的聚类算法。与划分和层次聚类方法不同，它将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类。 -DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a more represent
Kmeans_VS_EM_OnLaborDataSet
- 使用著名的数据挖掘和机器学习软件WEKA，在标准数据集labor上，比较Kmeans与EM算法。-Use well-known data mining and machine learning software WEKA, on standard data sets labor, relatively Kmeans with EM algorithm.
boost
- 介绍boost算法的原理及其变种，对于学习分类算法很有帮助。-Introduce the principle of the algorithm and its variants boost, helpful for learning classification algorithm.