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GMM-GMR-v1.2
- GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Ex
Classifier_gaussian
- The code is for Classification using Gaussian Method on the Multi Variate Dataset with no missing values.
DeepNeuralNetwork20131115
- It provides deep learning tools of deep belief networks (DBNs).-Run testDNN to try! Each function includes descr iption. Please check it! It provides deep learning tools of deep belief networks (DBNs) of stacked restricted Boltzmann machines (RB
深度神经网络
- It provides deep learning tools of deep belief networks (DBNs).-Run testDNN to try! Each function includes descr iption. Please check it! It provides deep learning tools of deep belief networks (DBNs) of stacked restricted Boltzmann machines (RBMs).
Bayes_Classifier
- Bayes classifier Step 1. : Generate an arbitrary 3-class dataset with bi-variate Gaussian distribution. Step 2. : If three arbitrary samples are given as follows, determine to which class (as each class is generated by Step 1) each sample s
Maximum-Likelihood-Estimation
- Maximum Likelihood Estimation Step 1. : Estimate the mean vector and covariance of an arbitrary 3-class dataset with bi-variate Gaussian distribution by maximum likelihood estimation An arbitrary 3-class dataset is given (by Dataset.mat) and t
mlclass-ex6
- 支持向量机，实现2或多分类，基于matlab仿真，内有说明-ex6.m- Octave scr ipt for the rst half of the exercise ex6data1.mat- Example Dataset 1 ex6data2.mat- Example Dataset 2 ex6data3.mat- Example Dataset 3 svmTrain.m- SVM rraining function svmPredict.m- SVM p
ridge
- 这段代码可以实现机器学习中的岭回归。自行添加高斯噪声后对每个数据集的七阶多项式用不同的值λ进行岭回归-Perform ridge regression on each dataset with 7th order polynomial with different value λ.Add Gaussian noise.
2
- （a）产生两个都具有200个二维向量的数据集和（注意：在生成数据集之前最好使用命令randn(‘seed’,0)初始化高斯随机生成器为0（或任意给定数值），这对结果的可重复性很重要）。向量的前半部分来自均值向量的正态分布，并且协方差矩阵。向量的后半部分来自均值向量的正态分布，并且协方差矩阵。其中是一个2*2的单位矩阵。 （b）在上述数据集上运用感知器算法，并且使用不同的初始向量初始化参数向量。 （c）测试每一次算法在和上的性能。 （d）画出数据集和，以及分类面。((a) Generate
L4_1
- a）产生两个都具有200个二维向量的数据集和（注意：在生成数据集之前最好使用命令randn(‘seed’,0)初始化高斯随机生成器为0（或任意给定数值），这对结果的可重复性很重要）。向量的前半部分来自均值向量的正态分布，并且协方差矩阵。向量的后半部分来自均值向量的正态分布，并且协方差矩阵。其中是一个2*2的单位矩阵。 （b）在上述数据集上和分别属于+1类和-1类，请在上述数据集的两类中各随机抽取150个样本作为训练集，运用Logistic regression算法得到的分类面，然后对余下的各5