在機器語言中，支持向量機可解決有限樣本學習的有效工具。最小二乘支持向量機(Least Square Support Vector regression，LSSVR)用於解決預測問題，其方法是採用最小二乘線性系統作為損失函數，代替傳統的支持向量機採用的二次規劃方法，能簡化了計算的複雜性及運算速度明顯快於支持向量機。本篇提出一種自動運算求解的最小平方支持向量迴歸機，架構主要分為三部分:第一部分為初始化參數包含混合式核函數參數與模型參數：考慮混沌序列因混沌運動，在初始化粒子群中粒子的位置時採用，加強演算法的搜索多樣性,為找到更優解和加快收斂奠定堅實的基礎。第二部分為特徵變數選擇：利用粒子群演算法進行特徵優化。第三部分為最優化參數：將第一部分初始化的解進行粒子群演算法求解，同時加入第二部分訓練的特徵變數，最後集體訓練學習成為最佳解，本研究把求解最小平方支持向量迴歸機的演算方法稱之CP- LSSVR。
在模擬驗證上，本篇採用UCI資料庫中不同類型的顯著資料測試並與多種演算法比較。實驗結果證實本篇所提出的CP- LSSVR 有較佳的預測能力。

In this study, we propose an automatic optimization least square support vector regression (LSSVR) using CPSO with a mixed kernel in order to solve regression problems. There are three parts for the LSSVR model. The first part is to consider the position of particles (solution) in chaotic sequence with good randomness and ergodic property of the characteristics in initial. The second part is the binary particle swarm optimization (PSO) employing to select possible input feature combination. Finally, a chaos search is used to select possible input features, and then we combine the optimize parameters optimized by PSO, called CP-LSSVR for short. For illustration and evaluation purposes, the CP- LSSVR is utilized to predict the remarkable datasets testing targets acquired from the UCI dataset. The results indicate that the proposed CP- LSSVR can produce a predicted model using a small number of features and show higher predictive capability than other methods listed in this paper.

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