A convex relaxation framework for a class of semi-supervised learning methods and its application in pattern recognition
We propose a semi-definite program relaxation framework for a class of S3VMs. One significant feature of this SDP framework is that it can implement L1-norm. This SDP framework needs only to solve the primal problems. The proposed approach is applied directly to identify the purity of maize seeds.
semi-supervised learning semi-definite programming support vector machine purity of hybrid seeds