中英
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linear classifier中文是什么意思
- 中文翻譯
- 造句
- 線性分類器
- linear adj. 1.線的，直線的。 2.長度的。 3.【數(shù)學】 ...
- classifier n. 1.分類者。 2.【礦物】分級機。 3.【化學】 ...
- piecewise linear classifier 分段線性分類器
- classifier n. 1.分類者。 2.【礦物】分級機。 3.【化學】分粒器。 4.(漢語等中的)量詞。
- be linear with 與...成線性關(guān)系
- linear adj. 1.線的，直線的。 2.長度的。 3.【數(shù)學】一次的，線性的。 4.【動、植】線狀的；細長的。 5.由線條組成的，以線條為主的，強調(diào)線條的。 linear amplification 直線放大。 a linear equation 一次方程式。 a linear leaf 線形葉。 linear arts 線條藝術(shù)。
- linear a 線形文字a; 線性文字a
- no-linear 非線性的
- adaptive classifier 自適應分類器
- air classifier 風力分級器; 風力化級器; 風篩; 空氣分級機; 空氣分級器; 空氣過濾器; 氣力分級器; 氣流分級機
- akins classifier 阿金斯分級機; 螺旋分級機
- annular classifier 環(huán)式分級機
- array classifier 陣列分類器
- atkins classifier 阿特金斯選粒機
- automatic classifier 自動分類機
- bayes classifier 貝斯分類符; 貝葉斯分類器
- bayesian classifier 貝葉斯分類器
- bowl classifier 分級槽; 浮槽分級機; 浮槽分級器; 浮槽式分級機
- cascade classifier 梯級式分選機
- centrifugal classifier 離心分級機; 離心分級器
- classifier fan 分級機風扇
- classifier gate 分選器擋板
- classifier overflow 分級機溢流
- classifier role 類元角色; 元角色
- classifier separator 分級機式分選機; 分級選礦機; 重力式粗粉分離
- 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向量模式的,即所有的模式都采用向量表示,要應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
在模式識別系統(tǒng)中,分類器是一個重要的組成部分,分類器設(shè)計的好壞將直接影響模式識別系統(tǒng)最終的識別性能。 - 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 )的人臉識別方法,以及基于pca和rbf的人臉識別方法,被用于和本文所提出的人臉識別方法進行比較。 - 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)絡結(jié)構(gòu)緊湊等優(yōu)點,被廣泛運用于模式識別問題中。 - 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
支持向量機是從線性可分的分類情況下提出的,并發(fā)展成用來解決非線性模式識別問題的有效手段,由于現(xiàn)實中存在的分類問題往往是非線性的,且可能無法完全分開,文中主要介紹了用于處理此類問題的非線性軟間隔分類機,即c -支持向量分類機,并介紹了一種改進的方法: -支持向量分類機,其參數(shù)具有一些直觀上的意義,避免了c -支持向量分類機中參數(shù)c沒有確切意義的缺點。
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.
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