KNN, namely k-nearest neighbor method, is a basic classification and regression method proposed by Cover T and Hart P in 1967. It is also one of the basic algorithms of machine learning.
This article's reference tutorial: Machine learning practice
Principle of KNN algorithm
In a sample data set, also known as the training sample set, and eac ...
Posted by compbry15 on Sun, 05 Dec 2021 20:11:28 -0800
Since the development of computer algorithms, there have been many different classifications. At present, the most widely used is the generalized swarm intelligent optimization algorithm. For example, particle swarm optimization (PSO), whale optimization (WOA), gray wolf optimization (GWO), dragonfly optimization (DA), uni ...
Posted by teejayuu on Sun, 05 Dec 2021 18:24:48 -0800
1) ID number
2) Diagnosis (M = malignant, B = benign)
Calculate 10 real value characteristics of each nucleus:
a) Radius (average distance from center to perimeter)
b) Texture (standard deviation of gray value)
e) Smoothness (local variation of radius length)
f) Compactness (perimeter ^ ...
Posted by ajlisowski on Sun, 05 Dec 2021 02:35:38 -0800
Data parameters OrderNumber: customer nickname LineNumber: purchase order. For example, the first three lines respectively represent three goods purchased by the same customer Model: trade name
Application of intelligent algorithm recommendation of association rules based on shopping basket.
Three basic ...
Posted by Skara on Sat, 04 Dec 2021 22:41:39 -0800
Atomization treatment can be represented by the following model:
I (x): foggy picture J (x): picture of defogging A : Global atmospheric light T (x): intermediate transformation mapping, depending on unknown depth information, medium transmission map
The previous defogging methods used r ...
Posted by carlos1234 on Thu, 02 Dec 2021 20:49:46 -0800
Original link: http://tecdat.cn/?p=24498In this example, we consider Markov transformation stochastic volatility model.statistical modelGive Way Are dependent variables and Unobserved log volatility The stochastic volatility model is defined as follows Zone variable Following a two-state Markov ...
Posted by msandersen on Thu, 02 Dec 2021 16:04:31 -0800
Ben delayed his sophomore electrical settings, and was lucky to join the team halfway. The author had not done camera and visual code before (AI electromagnetic), although there were various regrets in the end, but also temporarily learned a lot of new knowledge. Before the assessment, the foundation and development were completed s ...
Posted by Fractal on Thu, 02 Dec 2021 09:16:12 -0800
For guaranteed customer group, how to conduct detailed analysis and mining on the guaranteed customer group type? As shown in Figure 1, how do I get the label and how do I label it?
Figure 1: Sample Diagram
Using graph technology, you can label the triangle directly.
Guarantee association data cleaning; ...
Posted by hcoms on Wed, 01 Dec 2021 13:42:04 -0800
Bayesian classifier is a probability model, which uses Bayesian formula to solve the classification problem. Assuming that the feature vector of the sample obeys a certain probability distribution, we can calculate the conditional probability that the feature vector belongs to each class. The classification result is the one with the l ...
Posted by jtravis on Tue, 30 Nov 2021 17:46:05 -0800
PPASR streaming and non streaming speech recognition
The project will be divided into three stages: beginner ,Progressive class and Final stage Branch, which is currently the final level and continuously maintains the version. PPASR Chinese name PaddlePaddle Chinese speech recognition (PaddlePaddle Automatic Speech Recognition), is a PaddlePa ...
Posted by vitch2002 on Tue, 30 Nov 2021 08:54:09 -0800