Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a new unconstrained twin support vector regression model in the primal space (UPTSVR). With the addition of a regularization term in the formulation of the problem, the structural risk is minimized. The proposed formulation solves two smaller sized unconstrained minimization problems having continues, piece-wise quadratic objective functions by gradient based iterative methods. Hover, since their objective functions contain the non-smooth `plus' function, two approaches are taken: (i) replace the non-smooth `plus' function with their smooth approximate functions; (ii) apply a generalized derivative of the non-smooth `plus' function. They lead to five algorithms whose pseudo-codes are also given. Experimental results obtained on a number of interesting synthetic and real-world benchmark datasets using these algorithms in comparison with the standard support vector regression (SVR) and twin SVR (TSVR) clearly demonstrates the effectiveness of the proposed method.
['S. Balasundaram', 'Yogendra Meena']
I am writing a research paper on a idea Training primal twin support vector regression via unconstrained convex minimization
[{"from": "human", "value": "I am writing a research paper on a idea Training primal twin support vector regression via unconstrained convex minimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a new unconstrained twin support vector regression model in the primal space (UPTSVR). With the addition of a regularization term in the formulation of the problem, the structural risk is minimized. The proposed formulation solves two smaller sized unconstrained minimization problems having continues, piece-wise quadratic objective functions by gradient based iterative methods. Hover, since their objective functions contain the non-smooth `plus' function, two approaches are taken: (i) replace the non-smooth `plus' function with their smooth approximate functions; (ii) apply a generalized derivative of the non-smooth `plus' function. They lead to five algorithms whose pseudo-codes are also given. Experimental results obtained on a number of interesting synthetic and real-world benchmark datasets using these algorithms in comparison with the standard support vector regression (SVR) and twin SVR (TSVR) clearly demonstrates the effectiveness of the proposed method."}]