Rapid vectorization general multi-neighborhood data set classification method and system
A classification method and classification system technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems that the classification results are easily affected by noise data, the influence is too large, the cross of different types of data, etc., to improve real-time The effect of computing efficiency and improving parallel processing capability
Inactive Publication Date: 2021-09-24
ANHUI POLYTECHNIC UNIV MECHANICAL & ELECTRICAL COLLEGE
View PDF0 Cites 0 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Taking the selection of K value as an example, if the K value is too small, the classification result is easily affected by noise data; if the K value is too large, the influence between neighboring data points may be too large, resulting in crossover between different categories of data
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment Construction
[0035] The specific implementation manners of the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation manners described here are only used to illustrate and explain the embodiments of the present invention, and are not intended to limit the embodiments of the present invention.
[0036] figure 1 is a flow chart of a fast vectorized general multi-neighborhood data set classification method of the present invention, such as figure 1 As shown, the general multi-neighborhood data set classification method of the fast vectorization includes:
[0037] S101. Obtain a data set including category identification vector Species to be processed.
[0038] S102, extracting all unique categories from the category identification vector Species and sequentially numbering the vector matrix Ynumerical;
[0039] S103, obtain the total number of categories of each number in...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More PUM
Login to View More Abstract
The embodiment of the invention provides a rapid vectorization general multi-neighborhood data set classification method and system, and belongs to the technical field of computer science and statistics. According to the rapid vectorization general multi-neighborhood data set classification method and system, a data set given by a user is split into a training data set and a test data set which are randomly configured, and block operation is performed on all the data sets in a vector or matrix form, so that a large amount of cycle operation used in a classification process is avoided, the memory storage space is fully utilized, the parallel calculation efficiency is greatly improved, and the method is particularly suitable for real-time calculation of large-scale data classification.
Description
technical field [0001] The invention relates to the technical fields of computer science and statistics, in particular to a fast vectorized general multi-neighborhood data set classification method and system. Background technique [0002] The classification and recognition technology of supervised learning is one of the important branches in the field of computer machine learning. Compared with unsupervised learning, supervised learning can improve the classification and recognition accuracy of algorithms through sufficient big data information. With the rapid development of computer technology, Internet technology and electronic technology, the acquisition, storage and analysis of big data are becoming more and more convenient and fast, which further promotes the great progress of supervised learning technology. Neighborhood classification method is an important part of supervised learning technology. Its core idea is to classify data with similar distances into one catego...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More Application Information
Patent Timeline
Login to View More IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/241
Inventor 刘春静张磊
Owner ANHUI POLYTECHNIC UNIV MECHANICAL & ELECTRICAL COLLEGE