作业帮请把下面翻译成中文The use ofSVMs for density estimation (Weston et al
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请把下面翻译成中文
The use ofSVMs for density estimation (Weston et al., 1997) and ANOVA decomposition(Stit-son et al., 1997) has also been studied. Regarding extensions, the basicSVMs contain no prior knowledge of the problem (for example, a large class ofSVMs for the image recogni-tion problem would give the same results if thepixels were rst permuted randomly (with each image su ering the samepermutation), an act of vandalism that would leave the best performing neuralnetworks severely handicapped) and much work has been done on in-corporatingprior knowledge into SVMs (Scholkopf, Burges and Vapnik, 1996 Scholkopf et al.,1998a Burges, 1998). Although SVMs have good generalization performance, theycan be abysmally slow in test phase, a problem addressed in (Burges, 1996 Osunaand Girosi, 1998). Recent work has generalized the basic ideas (Smola,Scholkopf and Muller, 1998a Smola and Scholkopf, 1998), shown connections toregularization theory (Smola, Scholkopf and Muller, 1998b Girosi, 1998 Wahba,1998), and shown how SVM ideas can be incorpo-rated in a wide range of otheralgorithms (Scholkopf, Smola and Muller, 1998b Scholkop et al,1998c). The reader may also nd the thesis of (Scholkopf, 1997) helpful.
The use ofSVMs for density estimation (Weston et al., 1997) and ANOVA decomposition(Stit-son et al., 1997) has also been studied. Regarding extensions, the basicSVMs contain no prior knowledge of the problem (for example, a large class ofSVMs for the image recogni-tion problem would give the same results if thepixels were rst permuted randomly (with each image su ering the samepermutation), an act of vandalism that would leave the best performing neuralnetworks severely handicapped) and much work has been done on in-corporatingprior knowledge into SVMs (Scholkopf, Burges and Vapnik, 1996 Scholkopf et al.,1998a Burges, 1998). Although SVMs have good generalization performance, theycan be abysmally slow in test phase, a problem addressed in (Burges, 1996 Osunaand Girosi, 1998). Recent work has generalized the basic ideas (Smola,Scholkopf and Muller, 1998a Smola and Scholkopf, 1998), shown connections toregularization theory (Smola, Scholkopf and Muller, 1998b Girosi, 1998 Wahba,1998), and shown how SVM ideas can be incorpo-rated in a wide range of otheralgorithms (Scholkopf, Smola and Muller, 1998b Scholkop et al,1998c). The reader may also nd the thesis of (Scholkopf, 1997) helpful.
ofsvms for the use密度估计（Weston等人，1997年）和方差分解（stit他的等人，1997年）has also been studied。对扩展，the basicsvms包含没有prior knowledge of the problem（for example，大类ofsvms for the图像识别、问题would give the same results如果thepixels were RST（with each图像permuted randomly苏埃林格the samepermutation），年vandalism Act of that would leave the best表演和严重残疾人）and much work has been done我们在corporatingprior知识为支持向量机（scholkopf，汉堡和Vapnik等人，1996年scholkopf汉堡，1998年，1998年）。虽然有好的性能支持向量机的处理器，theycan be abysmally慢在测试阶段，问题解决在（汉堡osunaand girosi，1996年，1998年）。最近的工作已基本思想（广义smola，scholkopf and Muller，1998年smola and scholkopf，1998年），显示登录toregularization理论（smola，scholkopf and Muller，1998b girosi Wahba，1998年，1998年），and How can be shown SVM思想incorpo级in a wide range of otheralgorithms（scholkopf，smola和穆勒scholkop，1998b等人，1998年b）。读者may also Nd the Thesis of（scholkopf乐于助人，1997年）。
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