A support vectors classification Method Based on projection vector boundary feature is proposed. According to statistical theory and normal distribution characteristics in one-dimensional space, the proposed algorithm introduces a new definition of the margin, objective function is constructed in high-dimensional space, through solving the objective function, and projection line is obtained. After the training samples are projected to the line , we construct boundary vector sets in one-dimensional space, which are used to train support vector machine(SVM). Experiments on two artificial data sets and UCI standard data set show that the proposed method is effective.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.