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支援向量機 Support Vector Machine

支援向量機(Support Vector Machine, SVM)是一種監督式學習的方法,一般是應用於分類(Classification/supervised learning)等相關議題上。SVM 基本運作模式如下:在給定一群訓練樣本之下,每個樣本會分別對應至兩個不同的類別(Category),SVM 會嘗試從建構一個模型(Model),並利用此模型將每一個樣本分配到一個類別上。

A support vector machine (SVM) is a concept in computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes the input is a member of, which makes the SVM a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

來源、作者

基本概念

典型的SVM是一種二元分類器(Two-class classifier),因此,以下我們僅針對典型SVM來進行說明。

在二元分類中,SVM嘗試在訓練資料(x)所構成的空間中,尋找一個超平面(Hyperplane)能將不同類別的資料完美的分開,而且,希望此超平面與不同的類別的距離”愈大愈好”。如圖所示,藍色矩形為第一個類別(標記為+1),紅色圓形為第二個類別(標記為-1),而SVM則想要找出的超平面即是wx+b=0,此超平面可以使得兩個類別(邊界/Margin)的距離最大。

應用

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Cho-Wei Shih,
2011年8月8日 下午8:42
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