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
1. Background The development of the brain is influenced by multiple factors, with a higher socioeconomic status (higher socioeconomic status, SES) being one of the important factors. SES during childhood and adolescence is associated with stronger cognitive abilities, academic achievements, and lower rates of mental illness, and can even affect the development of the cerebral … Read more