Summary: In comparison with the dual formulation of \(\varepsilon\)-support vector machine, the dual of \(v\)-support vector regression (\(v\)-SVR) has an extra inequality constraint. To date, there is no effective and feasible \(v\) solution path for \(v\)-SVR. To solve the infeasible updating path problem of the \(v\) solution path for \(v\)-SVR, an improved \(v\) solution path for \(v\)-SVR is proposed. Based on the modified formulation of \(v\)-SVR and the Karush-Kuhn-Tucker (KKT) conditions, the strategy of using a new introduced variable and an extra term can avoid the conflicts and exceptions effectively during the adiabatic incremental adjustments. Finally, the proposed algorithm can fit the entire \(v\) solution path within the finite number of iterations. Theoretical analysis and simulation results demonstrate that the proposed algorithm is effective and feasible.