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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Linear classifier ： ウィキペディア英語版 Linear classifier In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector. Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (features), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use. == Definition == If the input feature vector to the classifier is a real vector $\backslash vec\; x$, then the output score is :$y\; =\; f(\backslash vec\backslash cdot\backslash vec)\; =\; f\backslash left(\backslash sum\_j\; w\_j\; x\_j\backslash right),$ where $\backslash vec\; w$ is a real vector of weights and ''f'' is a function that converts the dot product of the two vectors into the desired output. (In other words, $\backslash vec$ is a one-form or linear functional mapping $\backslash vec\; x$ onto R.) The weight vector $\backslash vec\; w$ is learned from a set of labeled training samples. Often ''f'' is a simple function that maps all values above a certain threshold to the first class and all other values to the second class. A more complex ''f'' might give the probability that an item belongs to a certain class. For a two-class classification problem, one can visualize the operation of a linear classifier as splitting a high-dimensional input space with a hyperplane: all points on one side of the hyperplane are classified as "yes", while the others are classified as "no". A linear classifier is often used in situations where the speed of classification is an issue, since it is often the fastest classifier, especially when $\backslash vec\; x$ is sparse. Also, linear classifiers often work very well when the number of dimensions in $\backslash vec\; x$ is large, as in document classification, where each element in $\backslash vec\; x$ is typically the number of occurrences of a word in a document (see document-term matrix). In such cases, the classifier should be well-regularized. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Linear classifier」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.