A distance mapping pattern classification method
The invention relates to a distance mapping pattern classification method. The established distance mapping classifier comprises four parts, namely an input layer, a hidden layer 1, a hidden layer 2 and an output layer. The input layer comprises d neuron nodes, and each node represents a feature vec...
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| creator | HOU HUIRANG MENG QINGHAO |
| description | The invention relates to a distance mapping pattern classification method. The established distance mapping classifier comprises four parts, namely an input layer, a hidden layer 1, a hidden layer 2 and an output layer. The input layer comprises d neuron nodes, and each node represents a feature vector of a training or test sample. The feature vector set of the extracted samples is used as the input layer of the distance mapping classifier. The hidden layer 1 contains N neuron nodes, N representing the total number of training samples; and calculates the Euclidean distance between the input eigenvector and the eigenvectors of N training samples as the hidden layer 1. The hidden layer 2 consists of L neuron nodes. The hidden layer 1 and the hidden layer 2 can be connected by linear activation function. The two parameter matrices of the linear activation function are the connection weights and offsets of the hidden layer 1 and 2 respectively. When the classifier model is used to classifythe test samples, the ou |
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The established distance mapping classifier comprises four parts, namely an input layer, a hidden layer 1, a hidden layer 2 and an output layer. The input layer comprises d neuron nodes, and each node represents a feature vector of a training or test sample. The feature vector set of the extracted samples is used as the input layer of the distance mapping classifier. The hidden layer 1 contains N neuron nodes, N representing the total number of training samples; and calculates the Euclidean distance between the input eigenvector and the eigenvectors of N training samples as the hidden layer 1. The hidden layer 2 consists of L neuron nodes. The hidden layer 1 and the hidden layer 2 can be connected by linear activation function. The two parameter matrices of the linear activation function are the connection weights and offsets of the hidden layer 1 and 2 respectively. When the classifier model is used to classifythe test samples, the ou</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2018</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20181211&DB=EPODOC&CC=CN&NR=108985315A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20181211&DB=EPODOC&CC=CN&NR=108985315A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HOU HUIRANG</creatorcontrib><creatorcontrib>MENG QINGHAO</creatorcontrib><title>A distance mapping pattern classification method</title><description>The invention relates to a distance mapping pattern classification method. The established distance mapping classifier comprises four parts, namely an input layer, a hidden layer 1, a hidden layer 2 and an output layer. The input layer comprises d neuron nodes, and each node represents a feature vector of a training or test sample. The feature vector set of the extracted samples is used as the input layer of the distance mapping classifier. The hidden layer 1 contains N neuron nodes, N representing the total number of training samples; and calculates the Euclidean distance between the input eigenvector and the eigenvectors of N training samples as the hidden layer 1. The hidden layer 2 consists of L neuron nodes. The hidden layer 1 and the hidden layer 2 can be connected by linear activation function. The two parameter matrices of the linear activation function are the connection weights and offsets of the hidden layer 1 and 2 respectively. When the classifier model is used to classifythe test samples, the ou</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2018</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDBwVEjJLC5JzEtOVchNLCjIzEtXKEgsKUktylNIzkksLs5My0xOLMnMz1PITS3JyE_hYWBNS8wpTuWF0twMim6uIc4euqkF-fGpxQWJyal5qSXxzn6GBhaWFqbGhqaOxsSoAQBuQCuK</recordid><startdate>20181211</startdate><enddate>20181211</enddate><creator>HOU HUIRANG</creator><creator>MENG QINGHAO</creator><scope>EVB</scope></search><sort><creationdate>20181211</creationdate><title>A distance mapping pattern classification method</title><author>HOU HUIRANG ; MENG QINGHAO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN108985315A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2018</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>HOU HUIRANG</creatorcontrib><creatorcontrib>MENG QINGHAO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HOU HUIRANG</au><au>MENG QINGHAO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>A distance mapping pattern classification method</title><date>2018-12-11</date><risdate>2018</risdate><abstract>The invention relates to a distance mapping pattern classification method. The established distance mapping classifier comprises four parts, namely an input layer, a hidden layer 1, a hidden layer 2 and an output layer. The input layer comprises d neuron nodes, and each node represents a feature vector of a training or test sample. The feature vector set of the extracted samples is used as the input layer of the distance mapping classifier. The hidden layer 1 contains N neuron nodes, N representing the total number of training samples; and calculates the Euclidean distance between the input eigenvector and the eigenvectors of N training samples as the hidden layer 1. The hidden layer 2 consists of L neuron nodes. The hidden layer 1 and the hidden layer 2 can be connected by linear activation function. The two parameter matrices of the linear activation function are the connection weights and offsets of the hidden layer 1 and 2 respectively. 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| language | chi ; eng |
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| subjects | CALCULATING COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | A distance mapping pattern classification method |
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