由於採用的地球原子不同，目前，台灣地區有兩種坐標系統存在，TWD67(Taiwan Datum 1967) 和TWD97(Taiwan Datum 1997)。在應用上，必須進行不同地球原子間之坐標轉換。坐標轉換方面，有許多方法可供選擇，如六參數轉換、支持向量機(Support Vector Machine, SVM)轉換等。 最小二乘支持向量機(Least Square Support Vector Machine, LSSVM)，為SVM的一種演算法，是一種非線性模型。LSSVM在運用上所需之參數少，能夠解決小樣本、非線性、高維度和局部極小點等問題。目前，LSSVM，已經被成功運用在影像分類和統計迴歸等領域上。 本研究將利用LSSVM採用不同之核函數：線性核函數(LIN)、多項式核函數(POLY)及徑向基核函數(RBF)進行TWD97和TWD67之坐標轉換。研究中並使用基因演算法來調整LSSVM的RBF核函數之系統參數(後略稱RBF+GA)，找出較佳之系統參數組合以進行坐標轉換。模擬與實測之地籍資料，將被用以測試LSSVM及六參數坐標轉換方法的轉換精度。 研究結果顯示，RBF+GA在各實驗區之轉換精度優於參數優化前RBF之轉換精度，且RBF+GA之轉換精度也較六參數轉換之轉換精度高。 進行參數優化後，RBF+GA相對於RBF的精度提升率如下：(1)模擬實驗區：參考點與檢核點數量比分別為1：1、2：1、3：1、1：2及1：3時，精度提升率分別為15.2%、21.9%、33.2%、12.0%、11.7%；(2)真實實驗區：花蓮縣、台中市及台北市實驗區之精度提升率分別為20.1%、32.4% 、22.5%。 / There are two coordinate systems with different geodetic datum in Taiwan region, i.e., TWD67 (Taiwan Datum 1967) and TWD97 (Taiwan Datum 1997). In order to maintain the consistency of cadastral coordinates, it is necessary to transform from one coordinate system to another. There are many coordinate transformation methods, such as, 2-dimension 6-parameter transformation, and support vector machine (SVM). Least Square Support Vector Machine (LSSVM), is one type of SVM algorithms, and it is also a non-linear model。LSSVM needs a few parameters to solve non-linear, high-dimension problems, and it has been successfully applied to the fields of image classification, and statistical regression. The goal of this paper is to apply LSSVM with different kernel functions (POLY、LIN、RBF) to cadastral coordinate transformation between TWD67 and TWD97. Genetic Algorithm will be used to find out an appropriate set of system parameters for LSSVM with RBF kernel to transform the cadastral coordinates. The simulated and real data sets will be used to test the performances, and coordinate transformation accuracies of LSSVM with different kernel functions and 6-parameter transformation. According to the test results, it is found that after optimizing the RBF parameters (RBF+GA), the transformation accuracies using RBF+GA are better than RBF, and even better than those of 6-parameter transformation. Comparing with the transformation accuracies using RBF, the transformation accuracy improving rate of RBF+GA are : (1) The simulated data sets: when the amount ratio of reference points and check points comes to 1:1, 2:1, 3:1, 1:2 and 1:3, the transformation accuracy improving rate are 15.2%, 21.9%, 33.2%, 12.0% and 11.7%, respectively; (2) The real data sets： the transformation accuracy improving rate of RBF+GA for the Hualien, Taichung and Taipei data sets are 20.1%, 32.4% and 22.5%, respectively.