例句與造句

1. based on the ensemble learning method, the outliers are learned and deleted from the training corpus
   本研究采用基于系綜學(xué)習(xí)的野點學(xué)習(xí)方法剔除網(wǎng)頁中的噪聲樣本，有效地提高了文本分類的性能。
2. ensemble learning algorithms can significantly improve the generalization ability of learning systems through training a finite number of weak learners and combine their results
   集成學(xué)習(xí)算法通過訓(xùn)練多個弱學(xué)習(xí)算法并將其結(jié)論進行合成，可以顯著地提高學(xué)習(xí)系統(tǒng)的泛化能力。
3. experiments show that our algorithms are especially useful for ensemble learning, and could achieve the lowest test error for many complex data sets when coupled with adaboost
   實驗表明，本演算法對集成學(xué)習(xí)特別有幫助，配合增強集成學(xué)習(xí)演算法后，可在許多復(fù)雜的資料上得到最佳的測試結(jié)果。
4. boosting is a representative algorithm of ensemble learning and has received a great deal of research and application, but it is mainly focused on the problem of classification
   boosting算法作為集成學(xué)習(xí)算法的主要代表算法，得到了廣泛的研究和應(yīng)用，但其研究成果大部分都集中的分類問題上。
5. through the experiment on the cod data and compared with some commonly used regression methods, it proves the efficiency and applicability of the ensemble learning boosting regression algorithm
   通過對一批水質(zhì)cod數(shù)據(jù)進行的實驗仿真，并將其結(jié)果和一些常用的回歸方法作了比較，證明了集成學(xué)習(xí)boosting回歸算法對各種實際問題的有效性和適應(yīng)性。
6. It's difficult to find ensemble learning in a sentence. 用ensemble learning造句挺難的
7. second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems . experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners
   由于多示例學(xué)習(xí)具有獨特的性質(zhì)，被認為是一種與監(jiān)督學(xué)習(xí)非監(jiān)督學(xué)習(xí)強化學(xué)習(xí)并列的一種新的學(xué)習(xí)框架。
8. second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems . experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners
   由于多示例學(xué)習(xí)具有獨特的性質(zhì)，被認為是一種與監(jiān)督學(xué)習(xí)非監(jiān)督學(xué)習(xí)強化學(xué)習(xí)并列的一種新的學(xué)習(xí)框架。