Source: You Er's Cabin – New Machine Vision This article is about 7000 words long and is recommended to read in 14 minutes. This article shares a piece about clustering: 10 clustering algorithms and their Python code. Clustering or cluster analysis is an unsupervised learning problem. It is commonly used as a data analysis technique … Read more
Source: DeepHub IMBA This article is about 6000 words long and is recommended to be read in 10+ minutes. This article will first explore mixture models, focusing on Gaussian mixture models and their fundamental principles. Gaussian Mixture Model (GMM) is a statistical model that represents data as a mixture of Gaussian (normal) distributions. These models … Read more
Hello everyone~ Today we are going to talk about a probabilistic model – Gaussian Mixture Model (GMM). Gaussian Mixture Model is like a complex fuzzy grouping mechanism, believing that a dataset is actually composed of multiple “small groups,” where things within each “small group” are roughly distributed according to a bell curve (Gaussian distribution). You … Read more
Recently, I have been reading the book “Machine Learning in Action” because I want to delve deeper into machine learning algorithms, and since I want to learn Python, I chose this book to study based on my friend’s recommendation. Before writing this article, I had some understanding of FCM, so I feel an inexplicable affinity … Read more
Background: The internationally recognized academic organization the IEEE International Conference on Data Mining (ICDM) selected the top ten classic algorithms in the field of data mining in December 2006:C4.5、k-Means、SVM、Apriori、EM、PageRank、AdaBoost、kNN、Naive Bayes and CART。 Not only are the selected ten algorithms classic, but actually any of the 18 algorithms that participated in the selection can also be … Read more
The k-means algorithm is one of the most commonly used methods for unsupervised clustering due to its simplicity and good applicability to large sample data. This article provides a detailed summary of the principles of the k-means clustering algorithm. Table of Contents 1. Principles of k-means Clustering Algorithm 2. Steps of k-means Clustering Algorithm 3. … Read more
1 Introduction K-means and kNN may both start with ‘k’, but they are two different types of algorithms—kNN is a classification algorithm in supervised learning, while k-means is a clustering algorithm in unsupervised learning. The similarity between the two is that both utilize neighbor information to label categories. Clustering is a very important learning paradigm … Read more
Author: Wang Jiaxin Reviewed by: Chen Zhiyan This article is about 5800 words and is recommended to read for more than 10 minutes. This article introduces the classic K-Means clustering algorithm. Overview As is well known, machine learning algorithms can be divided into supervised learning and unsupervised learning. Supervised learning is often used for classification … Read more
Author: Wang Student Source: Submission Editor: Senior Sister 1. Basic Concepts 1.1 KNN k-nearest neighbor (k-NN) is a basic classification and regression method. The input of k-nearest neighbor is the feature vector of the instance corresponding to the point in feature space; the output is the class of the instance, which can be multi-class. The … Read more
The Uster Quality Management Platform has brought significant changes to workshop personnel. Finding important information in a busy textile factory can be quick and easy—or it can be a tedious task that makes time feel like it’s dragging. The constant influx of emails only adds to the burden. This is precisely why Hoa Tho Textile, … Read more