What is the K-Nearest Neighbors Algorithm (KNN)? The K-Nearest Neighbors algorithm (KNN) is a simple and intuitive machine learning algorithm widely used for classification and regression tasks. Its core idea is based on the principle of “birds of a feather flock together,” finding the K most similar neighbors by comparing the distance between a new … Read more
(Click the public account above to quickly follow) Source: Qiu Kang singasong https://segmentfault.com/a/1190000006070219 Introduction I previously developed a campus dating APP, where one of the logics was to confirm that the user is a student by accessing their academic system. The basic idea was to use the user’s account and password to scrape the information. … Read more
Ultrasonic transducers have been widely used in object detection, non-destructive testing (NDT), biomedical imaging, and therapy. Compared to bulk ultrasonic transducers, piezoelectric micromachined ultrasonic transducers (PMUTs) with a small footprint have advantages such as low power consumption and wide bandwidth, making them suitable for various applications in consumer electronics and the Internet of Things (IoT), … Read more
Full Article Link: https://tecdat.cn/?p=33917 KNN is a non-parametric learning algorithm, which means it makes no assumptions about the underlying data. This is a very useful feature because most client data does not really follow any theoretical assumptions, such as linear separability, uniform distribution, etc. (Click the “Read the Original” link at the end of the … Read more
Improving kNN Matching Effect for Dating Websites This article follows up on the previous one, in which we briefly introduced the basic principles of the kNN algorithm. Using the context of dating website matching, we implemented data parsing from a text file using Python and created scatter plots with matplotlib for data analysis. In this … Read more
Nearest Neighbor Algorithm Algorithm Principles The principle of the nearest neighbor method is to find a predefined number of training samples that are closest to the new point and predict the label from them. The number of samples can either be a user-defined constant (k-nearest neighbor learning) or can vary based on the local density … Read more
I originally planned to work overtime for two days over the weekend, but suddenly other matters came up and were canceled. By the way, I looked at CSDN and saw an article introducing KNN. Since I am also working in the field of machine learning, I naturally need to understand some of this part. What … Read more
Table of Contents Understanding Nearest Neighbor Classification Step 1: Collecting Data Step 2: Exploring and Preparing Data Step 3: Training the Model Based on Data Step 4: Evaluating Model Performance Step 5: Improving Model Performance Understanding Nearest Neighbor Classification Do you know how proteins, vegetables, and fruits are classified? In life, we find that things … Read more
CodingGo Technical Community A Free Programming Learning Platform Algorithm Introduction KNN (K-Nearest Neighbors) is a simple machine learning algorithm that does not require learning any parameters and can be used for both classification and regression problems. The intuitive explanation of this algorithm is ‘Birds of a feather flock together.’ When a new sample is input, … Read more
“Birds of a feather flock together” is a common phenomenon in real life, indicating that similar things are likely to have similar attributes. Using this idea, machine learning can classify data, assigning it to the same category, such as similar or “nearest” neighbors. Today, let’s learn about nearest neighbor classification. 1. Understanding Nearest Neighbor Classification … Read more