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gaussianprocess4Clas
- 高斯过程是一种非参数化的学习方法，它可以很自然的用于regression，也可以用于classification。本程序用高斯过程实现分类！-Gaussian process is a non - parametric method of learning, it is very natural for regression. can also be used for classification. The procedures used to achieve classification G
Ex1
- 模式识别某次课程的作业，完成了高斯分布下的两种贝叶斯分类器,以及非参数的K近邻、Parzen窗方法，采用UCI机器学习数据库中的某些数据作为样本，使用交叉验证方法确定参数-Pattern recognition of a particular course work, completed under the two Gaussian Bayesian classifier, and the non-parametric K-nearest neighbor, Parzen window meth
parzen
- Parzen窗估计法是一种具有坚实理论基础和优秀性能的非参数函数估计方法，它能够较好地描述多维数据的分布状态。-Parzen window estimation method is a non-parametric function estimation method has a solid theoretical basis and excellent performance, it can be used to describe the distribution of state of th
KNN
- In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression
0-svnn
- 这段代码实现了一个新的MLP神经网络训练方法,来自论文A new method for neural network regularization（内附）-This code implements a new training method for MLP neural networks, named Support Vector Neural Network (SVNN), proposed in the work: O. Ludwig “Study on Non-parametric Me