资源列表
K-mean
- 聚类算法中的k-means算法，和k-medoids 肯定是非常相似的。k-medoids 和 k-means 不一样的地方在于中心点的选取，在 k-means 中，我们将中心点取为当前 cluster 中所有数据点的平均值。-Clustering algorithm k-means algorithm, and k-medoids certainly very similar. k-medoids and k-means not the same place that the center o
GMM
- 聚类算法之高斯混合模型，GMM 和 k-means 很像，不过 GMM 是学习出一些概率密度函数来（所以 GMM 除了用在 clustering 上之外，还经常被用于 density estimation ）。-Gaussian mixture model of clustering algorithm, GMM and k-means like, but GMM is learning some probability density function (so GMM except on cl
Machine-learning-demos-
- 所有机器学习方法的集合，功能很全面，效果很赞-All machine learning methods set features a very comprehensive, the effect is praise
fast_non_dominate
- matlab code for fast non dominated sorting genetic algorithm
BP-hehanshubiaoda
- 在Windows环境中利用Matlab实现BP学习算法在达到期望均方误差最小的情况下正确表达傅里叶核函数。-BP learning algorithm using Matlab achieve in the Windows environment to achieve the desired mean square error at minimal correct expression of Fourier kernels.
language-model
- 使用神经网络的方法进行语言建模，对于自然语言处理的方向的同学很有帮助！-using neural network to build the language model.
Finding_structure_in_time
- 采用RNN的模型进行时间序列分析，对于RNN感兴趣的同学很有帮助-using recurrent neural network to solve time series analysis problem.
rnn_review
- RNN回顾的论文，对于想了解RNN历史以及进展的同学有很大的帮助-RNN review paper, for that want to understand the history and current development of rnn.
ANN
- 利用BP神经网络实现数值拟合，并对模型性能进行评价-Using BP neural network to complete numerical fitting, and uating the model performance
SOFM
- 利用自组织映射神经网络进行分类并评价模型性能-Using SOFM to realize the classfication and uating the model performance
chuangjianshizhizhongqun
- 创建实数值种群避免了二进制种群的编码与解码指定种群的大小及每个个体的范围-To create real value population to avoid the binary population of encoding and decoding specifies the size of the population and the scope of each individual
shangchuanmutate
- 执行种群中个体的变异并在新种群中返回变异后的个体属于高级变异函数-Execute individual variation in a population and return in the new population mutated individuals belong to senior variation function