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info.json
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{
"abstract": "Incremental Support Vector Machines (SVM) are instrumental in\npractical applications of online learning. This work focuses on the\ndesign and analysis of efficient incremental SVM learning, with the\naim of providing a fast, numerically stable and robust\nimplementation. A detailed analysis of convergence and of\nalgorithmic complexity of incremental SVM learning is carried out.\nBased on this analysis, a new design of storage and numerical\noperations is proposed, which speeds up the training of an\nincremental SVM by a factor of 5 to 20. The performance of the new\nalgorithm is demonstrated in two scenarios: learning with limited\nresources and active learning. Various applications of the\nalgorithm, such as in drug discovery, online monitoring of\nindustrial devices and and surveillance of network traffic, can be\nforeseen.",
"authors": [
"Pavel Laskov",
"Christian Gehl",
"Stefan Kr{{\\\"u}}ger",
"Klaus-Robert M{{\\\"u}}ller"
],
"id": "laskov06a",
"issue": 69,
"pages": [
1909,
1936
],
"title": "Incremental Support Vector Learning: Analysis, Implementation and Applications",
"volume": "7",
"year": "2006"
}