Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AbstractProximal support vector machine via generalized eigenvalue (GEPSVM) is a recently proposed binary classiﬁcation technique which aims to seek two nonparallel planes so that each one is closest to one of the two datasets while furthest away from the other. ,  proposed a novel method called Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue (MRGEPSVM), which incorporates local geometry information within each class into GEPSVM by regularization technique. Each plane is required to ﬁt each dataset as close as possible and preserve the intrinsic geometric structure of each class via manifold regularization. MRGEPSVM is also extended to the nonlinear case by kernel trick. The effectiveness of the method is demonstrated by tests on some examples as ll as on a number of public data sets. These examples show the advantages of the proposed approach in both computation speed and test set correctness.
['Jun Liang', 'Fei-yun Zhang', 'Xiaoxia Xiong', 'Xiaobo Chen', 'Long Chen', 'Guo-hui Lan']
I am writing a research paper on a idea Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue
[{"from": "human", "value": "I am writing a research paper on a idea Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AbstractProximal support vector machine via generalized eigenvalue (GEPSVM) is a recently proposed binary classi\ufb01cation technique which aims to seek two nonparallel planes so that each one is closest to one of the two datasets while furthest away from the other. ,  proposed a novel method called Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue (MRGEPSVM), which incorporates local geometry information within each class into GEPSVM by regularization technique. Each plane is required to \ufb01t each dataset as close as possible and preserve the intrinsic geometric structure of each class via manifold regularization. MRGEPSVM is also extended to the nonlinear case by kernel trick. The effectiveness of the method is demonstrated by tests on some examples as ll as on a number of public data sets. These examples show the advantages of the proposed approach in both computation speed and test set correctness."}]