{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:28:17Z","timestamp":1740461297222,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>Aiming at problems of the traditional weight analysis method, such as its subjection to the lack of a large number of known samples or experts' individual subjective consciousness, and according to properties of small sample theory and good generalization of support vector machine (SVM), a factor weight analysis algorithm based on SVM is presented in this paper. Firstly, according to the SVM model, weights of various influence factors over different time periods are calculated. Then, the variation of each weight over time is tracked. Finally, the change rule of each factor's weight is analyzed. This approach is applied to study the change regulation of factors' weights, which influenced the Enteromorpha disaster of the Yellow Sea in the year of 2013. The weight of each factor over different time periods is obtained, and various stages of the disaster process are divided according to the change regulation of weights. The above research works are proved using the data from 2012, and all results above show that the algorithm using SVM to get weight dynamically is effective and reliable.<\/jats:p>","DOI":"10.3233\/978-1-61499-619-4-346","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:59:50Z","timestamp":1740398390000},"source":"Crossref","is-referenced-by-count":0,"title":["Weight Analysis of Influence Factors Based on SVM"],"prefix":"10.3233","author":[{"family":"He Shi-Jun","sequence":"additional","affiliation":[]},{"family":"Tang Ying-Li","sequence":"additional","affiliation":[]},{"family":"Zhang Ting","sequence":"additional","affiliation":[]},{"family":"Li Yu","sequence":"additional","affiliation":[]},{"family":"Xie Sheng-Dong","sequence":"additional","affiliation":[]},{"family":"He Pei-Min","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy System and Data Mining"],"original-title":[],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T12:11:29Z","timestamp":1740399089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-618-7&spage=346&doi=10.3233\/978-1-61499-619-4-346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-619-4-346","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}