资源列表
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
chapter20
- LIBSVM-FarutoUltimate工具箱及GUI版本介绍与使用-LIBSVM-FarutoUltimate toolbox and GUI versions introduction and use
chapter19
- 基于SVM的手写字体识别 -SVM-based handwriting recognition
chapter18
- 基于SVM的图像分割-真彩色图像分割-SVM-based image segmentation- true color image segmentation
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
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.
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
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
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
chapter13
- LIBSVM参数实例详解 -Detailed examples LIBSVM parameters
chapter11
- 离散Hopfield神经网络的分类——高校科研能力评价-Classification of discrete Hopfield neural networks- Research Capacity Evaluation
chapter8
- GRNN网络的预测----基于广义回归神经网络的货运量预测-GRNN network---- forecast based on generalized regression neural network cargo forecasts