input: K代表分类个数,然后是training set,由于是unsupervised learning,这里的训练集是没有打label的。这里的训练集数据时N维数据,并没有使用我们之前经常使用的方法去设置常数项。 下面我们使用K代表分类个数,k代表1-K中间的index,c的上标i表示第i个training example,它表示第i个数据的分类结果,μ表示每次的中心...
Andrew Ng<Machine Learning>学习笔记——Clustering聚类 Unsupervised Learning_Introduction 对于一个典型的有监督学习,我们的数据输入是以下形式的: {(x(i),y(i))|i=1,2,...m},其中y(i)是标签。我们的目标是找到一个决策边界能够正确的划分正负样本。我们一般通过拟合一个虚拟函数(Hypothesis Function)来达到...
soft assignment,elastic shape, learning weights 5.多维高斯分布如何表示? 对于二维高斯分布,一般用contour plot来表示,因为2d的更容易表示一些。 6.二维高斯分布的协方差矩阵如何影响它的分布? 方向和方差。 举个例子: 7.mixture model可以看作对KMeans的extension吗? KMeans只注重mean,而mixture model除了mean还注...
Learning Outcomes: By the end of this course, you will be able to:(通过本章的学习,你将掌握) -Create a document retrieval system using k-nearest neighbors.用K近邻构建文本检索系统 -Identify various similarity metrics for text data.文本相似性矩阵 -Reduce computations in k-nearest neighbor search ...
NumPy is a library for working with arrays and matricies in Python, you can learn about the NumPy module in our NumPy Tutorial.scikit-learn is a popular library for machine learning.Create arrays that resemble two variables in a dataset. Note that while we only use two variables here, this...
machine-learningclusteringmachine-learning-algorithmscluster-analysisclustering-algorithmclustering-evaluation UpdatedMay 13, 2025 Jupyter Notebook Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, ...
In Machine Learning there is 3 main types Supervised learning: Machine gets labelled inputs and their desired outputs, example we can say as Taxi Fare detection. Unsupervised learning: Machine gets inputs without desired outputs, Example we can say as Customer Segmentation...
similarities in their data values, or features. This kind of machine learning is considered unsupervised because it doesn't make use of previously known label values to train a model. In a clustering model, the label is the cluster to which the observation is assigned, based only on its ...
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仅列出核心代码: 1.findClosestCentroids.m m = size(X, 1); len = zeros(K, 1); for i = 1:m for j = 1:K len(j) = norm(X(i, :) - centroids(j, :))^2; end [~, idx(i)] = min(len); end 2.computeCentroids.m