separating hyperplane 中文意思是什麼
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The separating hyperplane of traditional support vector machines is sensitive to noises and outliers
摘要傳統的支持向量機分類超平面對噪聲和野值非常敏感。 -
Svm maps input vectors nonlinearly into a high dimensional feature space and constructs the optimum separating hyperplane in the spade to realize modulation recognition
支撐矢量機把各個識別特徵映射到一個高維空間，並在高維空間中構造最優識別超平面分類數據，實現通信信號的調制識別。 -
The multiple - hyperplane classifier, which is investigated from the complexity of optimization problem and the generalization performance, is the explicit extension of the optimal separating hyperplanes classifier
多超平面分類器從優化問題的復雜度和運行泛化能力兩方面進行研究，是最優分離超平面分類器一種顯而易見的擴展。 -
For this problem, a separating hyperplane is designed with the principle of maximizing the distance between two class centers, and a novel support vector machine, called maximal class - center margin support vector machine ( mccm - svm ) is designed
為了解決這個問題，本文以兩個類中心距離最大為準則建立分類超平面，構造一個新的支持向量機，稱作類中心最大間隔支持向量機。 -
By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built, svm presents a lot of advantages for resolving the small samples, nonlinear and high dimensional pattern recognition, as well as other machine - learning problems such as function fitting
Svm的基本思想是通過非線性變換將輸入空間變換到一個高維空間，然後在這個新的空間中求取最優分類超平面。它在解決小樣本、非線性及高維模式識別問題中表現出許多特有的優勢，並能夠推廣應用到函數擬合等其他機器學習問題中。 -
The idea is proposed that those increased date, which near the separating hyperplane, is significant for the forming of the new hyperplane, whenever these date are classed by the former hyperplane to test error set berr or test right set bok
與傳統的增量學習方法不同，本文中，作者認為那些在分類面邊緣增加的數據對分類面的改變都起著重要的作用，無論這些數據被初碩士論文支持向量機在圖像處理應用中若干問題研究始分類器p劃分到測試錯誤集berr或者測試正確集b 。 -
The separating plane with maximal margin is the optimal separating hyperplane which has good generation ability. to find a optimal separating hyperplane leads to a quadratic programming problem which is a special optimization problem. after optimization all vectors are evaluated a weight. the vector whose weight is not zero is called support vector
而尋找最優分類超平面需要解決二次規劃這樣一個特殊的優化問題，通過優化，每個向量（樣本）被賦予一個權值，權值不為0的向量稱為支持向量，分類超平面是由支持向量構造的。
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