Unlock instant, AI-driven research and patent intelligence for your innovation.

Nearest neighbor classification device and method

A nearest neighbor classification and nearest neighbor technology, applied in the information field, can solve problems such as poor robustness and noise sensitivity, and achieve the effect of improving accuracy and strong robustness

Inactive Publication Date: 2017-05-10
FUJITSU LTD
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The simple nearest neighbor classification strategy is less robust and more sensitive to noise. In order to solve this problem, many improvements have been made

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 more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Nearest neighbor classification device and method
  • Nearest neighbor classification device and method
  • Nearest neighbor classification device and method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] image 3 is a schematic diagram of the composition of the nearest neighbor classification device in Embodiment 1 of the present invention. Such as image 3 As shown, the device 300 includes:

[0026] An acquisition unit 301, configured to obtain K nearest neighbor samples of the test sample, where K is a positive integer;

[0027] A grouping unit 302, configured to group according to the categories of the K nearest neighbor samples, wherein each group corresponds to each category;

[0028] A first calculation unit 303, configured to calculate the weight of each group;

[0029] The second calculation unit 304 is used to calculate the probability density distribution of each group, and calculate the prior probability of the test sample for each group according to the probability density distribution of each group;

[0030] The third calculation unit 305 is used to calculate the score of the category corresponding to each group according to the weight of each group and...

Embodiment 2

[0075] The embodiment of the present invention also provides an electronic device, Figure 5 It is a schematic diagram of the composition of the electronic device of Embodiment 2 of the present invention. Such as Figure 5 As shown, the electronic device 500 includes a nearest neighbor classifying device 501, wherein the structure and function of the nearest neighbor classifying device 501 are the same as those described in Embodiment 1, and will not be repeated here.

[0076] Figure 6 It is a schematic block diagram of the system configuration of the electronic device according to Embodiment 2 of the present invention. Such as Figure 6 As shown, the electronic device 600 may include a central processing unit 601 and a memory 602; the memory 602 is coupled to the central processing unit 601. This diagram is exemplary; other types of structures may also be used, in addition to or in place of this structure, for telecommunications or other functions.

[0077] Such as Fi...

Embodiment 3

[0091] An embodiment of the present invention also provides a nearest neighbor classification method, which corresponds to the nearest neighbor classification device in Embodiment 1. Figure 7 It is a flowchart of the nearest neighbor classification method in Embodiment 3 of the present invention. Such as Figure 7 As shown, the method includes:

[0092] Step 701: Obtain the K nearest neighbor samples of the test sample, where K is a positive integer;

[0093] Step 702: grouping according to the category of the K nearest neighbor samples, where each group corresponds to each category;

[0094] Step 703: Calculate the weight of each group;

[0095] Step 704: Calculate the probability density distribution of each group, and calculate the prior probability of the test sample for each group according to the probability density distribution of each group;

[0096] Step 705: Calculate the score of the category corresponding to each group according to the weight of each group and...

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

No PUM Login to View More

Abstract

The embodiment of the invention provides a nearest neighbor classification device and method. Grouping is performed according to the categories of K nearest neighbor samples of a test sample, according to the weight of each group and the prior probability of the test sample to each group, the score of the category corresponding to each group is calculated, and the category with the highest score is determined to be the category of the test sample. Thus, since when the test sample is classified, the two factors of the weight and the prior probability of each category are considered, accuracy of a classification result can be effectively improved, and relatively high robustness is achieved.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a nearest neighbor classification device and method. Background technique [0002] With the continuous development of information technology, the application of pattern recognition is becoming more and more common. Nearest neighbor classification is a commonly used classification strategy in the field of pattern recognition. Simple nearest neighbor classification refers to that, for an object to be classified, K nearest neighbor training samples are selected according to some distance rules, and the category of the object is determined to be the most common category among the K nearest neighbor training samples , when K is 1, the category of the object is determined as the category of the single nearest neighbor sample. [0003] The simple nearest neighbor classification strategy is less robust and more sensitive to noise. In order to solve this problem, many impro...

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
no application Login to View More
IPC IPC(8): G06K9/62
CPCG06F18/24147
Inventor 刘汝杰
Owner FUJITSU LTD