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risk minimization in Chinese

Pronunciation:
How to pronounce "risk minimization""risk minimization" in a sentence

Translationmobile phoneMobile

  • 風險極小化
  • 風險減至最小限度。

Examples

  • It can solve small - sample learning problems better by using experiential risk minimization in place of structural risk minimization
    由于采用了使用結(jié)構(gòu)風險最小化原則替代經(jīng)驗風險最小化原則，使它較好的解決了小樣本學習的問題。
  • Because of the lack of training samples , traditional methods based on experiential risk minimization can not play well in recognizing the characters
    由于訓練樣本不足,決定了采用傳統(tǒng)的基于經(jīng)驗風險最小化原則的識別方法難以取得較好的識別效果。
  • Because neural network is based upon empirical risk minimization and asymptotic theories , it is suitable to deal with situations where the amount of samples is tremendous and even infinite
    神經(jīng)網(wǎng)絡(luò)的理論基礎(chǔ)是最小化經(jīng)驗誤差，這種基于傳統(tǒng)的漸進理論的學習方法，在訓練樣本點無窮多時是適用的。
  • Structure risk minimization based weighted partial least - squared method weighted partial least - squared wpls method was proposed to achieve structure risk minimization in the partial least - squares modeling process
    為了在偏最小二乘法pls建模過程中實現(xiàn)結(jié)構(gòu)風險最小化srm ，提出基于結(jié)構(gòu)風險最小化的加權(quán)偏最小二乘法wpls 。
  • Aimed at the character of the agriculture system , the least squares support vector machine prediction model is given based on the principle of the statistical learning theory and structural risk minimization
    針對農(nóng)業(yè)生產(chǎn)系統(tǒng)的特征，在統(tǒng)計學習理論和結(jié)構(gòu)風險最小化原理的基礎(chǔ)上，建立了基于最小二乘支持向量機的時間預測模型。
  • 1 . a modified denoising method based on vc dimension and wavelet package is presented , improving the shortcomings of denoising methods based on empirical risk minimization and wavelets thresholds
    針對傳統(tǒng)的基于經(jīng)驗風險最小化信號消噪方法和現(xiàn)有的小波閾值信號消噪方法的不足，基于統(tǒng)計學習理論，提出了一種改進的vc維小波包信號消噪方法。
  • Based on analysis of the conclusions in the statistical learning theory , especially the structural risk minimization and the - insensitive loss function , a novel linear programming support vector regression is proposed
    摘要通過對統(tǒng)計學習理論中的支持向量回歸問題，特別是結(jié)構(gòu)風險問題和-不敏感函數(shù)的分析，得到了一種新的支持向量回歸算法。
  • An novel support vector regression ( svr ) algorithm based on structural risk minimization inductive principle instead of empirical risk minimization principle was firstly introduced in well logs intelligent analysis
    摘要基于核學習的支持向量機，是一種采用結(jié)構(gòu)風險最小化原則代替?zhèn)鹘y(tǒng)經(jīng)驗風險最小化原則的新型統(tǒng)計學習方法，具有完備的理論基礎(chǔ)。
  • Support vector machine ( svm ) is a new method for pattern recognition based on the statistical learning theory . it is an implementation of structure risk minimization principle in the statistical learning theory
    支持向量機( svm )是在統(tǒng)計學習理論基礎(chǔ)上發(fā)展起來的一種新的模式識別方法，它是統(tǒng)計學習理論中的結(jié)構(gòu)風險最小化思想在實際中的一種體現(xiàn)。
  • Support vector machines ( svm ) are a kind of novel machine learning methods . it can solve small - sample learning problems better by using experiential risk minimization in place of structural risk minimination
    支持向量機( supportvectormachines ，簡稱svm )是在統(tǒng)計學習理論的基礎(chǔ)上發(fā)展起來的一種新的學習方法，它已初步表現(xiàn)出很多優(yōu)于已有方法的性能。
  • More examples：  1  2  3

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