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linear classifier in Chinese

Pronunciation:
How to pronounce "linear classifier""linear classifier" in a sentence

Translationmobile phoneMobile

  • 線性分類器

Examples

  • There are many conventional classifiers , which are generally divided into two categories : linear classifiers and nonlinear ones
    分類器一般分為線性分類器和非線性分類器。
  • The operating object of all these linear classifiers is vector pattern , i . e . , before applying them , any non - vector pattern should be firstly vectorized into a vector pattern
    然而現(xiàn)有的線性分類器幾乎都是針對(duì)向量模式的,即所有的模式都采用向量表示,要應(yīng)用于矩陣表示的模式,必須首先將矩陣模式轉(zhuǎn)換成向量模式。
  • These three classifiers are a linear classifier based on fuzzy features , a hierarchy classifier based on features of geometry definitions and a distance classifier based on frequency features of stroke curvature
    使用的分類器分別為基于模糊特征的線性分類器、使用幾何定義特征的分層分類器以及基于曲度頻域特征的距離分類器。
  • Classifier is an important ingredient in pattern recognition . among all the classifiers , linear classifiers are paid great attention in statistical pattern recognition due to their simplicity and easy expansibility to nonlinear classifiers
    在模式識(shí)別系統(tǒng)中,分類器是一個(gè)重要的組成部分,分類器設(shè)計(jì)的好壞將直接影響模式識(shí)別系統(tǒng)最終的識(shí)別性能。
  • The comparative performances are studied among the nmfs + rbf method , the pca + rbf method , and the pca + fld ( fisher ' s linear discriminant ) method . all simulations are carried out on the orl face database . the simulation results show that rbf classifier outperforms k - nearest neighbor linear classifier significantly in recognizing faces with occlusions , and the holistic representations are generally less sensitive to occlusions or noise than parts - based representations
    為了驗(yàn)證本文所提出的nmfs + rbf算法的性能,經(jīng)典的基于pca和fisher線性判別( fisher ' slineardiscriminant , fld )的人臉識(shí)別方法,以及基于pca和rbf的人臉識(shí)別方法,被用于和本文所提出的人臉識(shí)別方法進(jìn)行比較。
  • Radial basis function ( rbf ) neural networks , which can be used as non - linear classifiers , have been applied extensively in pattern classification because of their salient features in being universal approximators , possing the best approximation property and the fast learning speed and having more compact topology than other neural networks
    徑向基( radialbasisfunction , rbf )神經(jīng)網(wǎng)絡(luò)分類器,作為非線性分類器,具有全局函數(shù)逼近、函數(shù)擬合度好、收斂速度快、網(wǎng)絡(luò)結(jié)構(gòu)緊湊等優(yōu)點(diǎn),被廣泛運(yùn)用于模式識(shí)別問(wèn)題中。
  • Svm method is presented on the condition of linear classifier , and has been developed as an effective way in solving problems of nonlinear pattern recognition . in consideration that the problem of classification existing in reality is always nonlinear , and unable to be separated completely , a minute explanation of c - support vector classification ( c - svc ) is given and - support vector classification ( - svc ) - - a method for improvement in which parameter has objective significance is also introduced to avoid the disadvantage that parameter c in c - svc is of no exact significance
    支持向量機(jī)是從線性可分的分類情況下提出的,并發(fā)展成用來(lái)解決非線性模式識(shí)別問(wèn)題的有效手段,由于現(xiàn)實(shí)中存在的分類問(wèn)題往往是非線性的,且可能無(wú)法完全分開,文中主要介紹了用于處理此類問(wèn)題的非線性軟間隔分類機(jī),即c -支持向量分類機(jī),并介紹了一種改進(jìn)的方法: -支持向量分類機(jī),其參數(shù)具有一些直觀上的意義,避免了c -支持向量分類機(jī)中參數(shù)c沒(méi)有確切意義的缺點(diǎn)。

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