hyperplane 中文意思是什麼
hyperplane
解釋
n. 名詞 【數學】超平面。-
Research on distance from point in to hyperplane in euclidean space
歐氏空間中點到超平面的距離研究 -
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。 -
When traditional support vector machines separate data containing noises, the obtained hyperplane is not an optimal one
使用傳統的支持向量機對含有噪聲的數據分類時，所得到的超平面往往不是最優超平面。 -
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。 -
Chapter 2 has systematically discussed machine learning problem, which is the basic of svm, with statistical learning theory or slt. secondly, chapter 3 has educed the optimal hyperplane from pattern recognition
第二章探討了支持向量機理論基礎? ?學習問題，尤其是對vapnik等人的統計學習理論（ slt ）結合學習問題作了系統的闡述。 -
Pcc takes the normal vector of a hyperplane as the projecting direction, onto which the algebraic sum of all samples " projections is maximized, such that samples in one class can be separated well from the other by this hyperplane
主分量分類器是在兩類樣本投影代數和最大的前提下，獲得最佳投影方向（分類面法方向） ，實現樣本分類。它的不足之處在於： 1 -
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。 -
Is that if a set of points in n - space is cut by a hyperplane, then the application of the perceptron training algorithm will eventually result in a weight distribution that defines a tlu whose hyperplane makes the wanted cut
）下的結論是，如果n維空間的點集被超平面切割，那麼感知器的培訓演算法的應用將會最終導致權系數的分配，從而定義了一個tlu ，它的超平面會進行需要的分割。 -
In addition, all the system states are on the sliding hyperplane at the initial instant, the reaching phase of smc is eliminated and the global robustness and stability of the closed - loop system can be guaranteed with the proposed control strategy
此外，控制策略使得系統的初始狀態已經處于滑模面上，從而消除了滑模控制的到達階段，進而確保了閉環系統的全局魯棒性和穩定性。 -
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。 -
In chapter 4 we obtain the helly number for hyperplane transversal to translates of a convex cube in r ~ ( d ). where we prove that the helly number for such families is 5 when d = 2, and is greater than or equal to d + 3 when d 3
在第4章中我們探討了o中超平面橫截單位立方體平移形成的集族的heily數，證得碑中此heily數為5 ，在呼中此heily數z民並推廣至呼，在胸中此heily數d 3 -
If these points can be cut by a hyperplane - in other words, an n - dimensional geometric figure corresponding to the line in the above example - then there is a set of weights and a threshold that define a tlu whose classifications match this cut
如果這些點可以被超平面換句話說，對應于上面示例中的線的n維的幾何外形切割，那麼就有一組權系數和一個閾值來定義其分類剛好與這個切割相匹配的tlu 。 -
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 。 -
A geometric transversal is defined to be an affine subspace ( such as a point, a line, a plane, or a hyperplane ) intersecting every member of a given family. in part i we discuss three kinds of such problems. in chapter 2 we discuss point transversal to a family of translates of a convex sets in the plane, where we prove a famous conjecture of griinbaum ' s by a concrete and straightforward method for some special cases
如果一仿射子空間（如一個點，一條直線，一個平面，或一個超平面）與一給定集族的每一個元都相交，則我們稱該仿射子空間為該給定集族的一個幾何橫截（點橫截，直線橫截，平面橫截等） ，也稱該仿射子空間橫截該給定集族。 -
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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