首页|New Support Vector Machines Study Results Reported from University of West Flori da (Applying Multi-class Support Vector Machines: One-vs.-one Vs. One-vs.-all On the Uwf-zeekdatafall22 Dataset)
New Support Vector Machines Study Results Reported from University of West Flori da (Applying Multi-class Support Vector Machines: One-vs.-one Vs. One-vs.-all On the Uwf-zeekdatafall22 Dataset)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Support Vector Ma chines are discussed in a new report. According to news reporting originating fr om Pensacola, Florida, by NewsRx correspondents, research stated, “This study in vestigates the technical challenges of applying Support Vector Machines (SVM) fo r multi-class classification in network intrusion detection using the UWF-ZeekDa taFall22 dataset, which is labeled based on the MITRE ATT&CK framew ork. A key challenge lies in handling imbalanced classes and complex attack patt erns, which are inherent in intrusion detection data.”
PensacolaFloridaUnited StatesNorth and Central AmericaCybersecurityEmerging TechnologiesMachine LearningSu pport Vector MachinesVector MachinesUniversity of West Florida
NETL
NSTL
