基于RBF神经网络的回归分析的matab实现

机器学习之朴素贝叶斯分类+拉普拉斯平滑 资源内容：西瓜数据集3.0+朴素贝叶斯分类（python手写） 代码可直接运行 输入数据为DataFrame类型，比较好理解。

机器学习实战数据集def loadDataSet(): 3 dataMat = [] ; labelMat = [] 4 fr = open('testSet.txt') 5 for line in fr.readlines(): # 对文件的数据进行按行遍历 6 lineArr = line.strip().split() 7 dataMat.append([1.0, float(lineArr [0]), float(lineArr[1])]) 8 labelMat.append(int(lineArr[2])) # 数据的类别号列表丰富

糖尿病数据集

鸢尾花数据集可能是模式识别、机器学习等领域里被使用最多的一个数据集了，很多教材用这份数据来做案例 鸢尾花数据集共收集了三类鸢尾花，即Setosa鸢尾花、Versicolour鸢尾花和Virginica鸢尾花，每一类鸢尾花收集了50条样本记录，共计150条。 数据集包括4个属性，分别为花萼的长、花萼的宽、花瓣的长和花瓣的宽

NASA关于涡扇发动机的仿真数据集，以文本形式打包，包含readme文件。可用于人工智能、机器学习等领域。 Engine degradation simulation was carried out using C-MAPSS. Four different were sets simulated under different combinations of operational conditions and fault modes. Records several sensor channels to characterize fault evolution. The data set was provided by the Prognostics CoE at NASA Ames.

一个比较好的用于自学和研究HMM性质的文档

通过个人的理解完成部分代码并加上部分注释，完成决策树流程框图，展示决策树递归流程并进行可视化。数据来自《模式识别》（张学工），代码主要来自《机器学习实战》，侵权删。

机器学习的各种定义，你更倾向哪种定义呢？

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.