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38篇 您的检索式：作者名="SCHLKOPF"

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	题名	作者	年代	出处	被引量
1	Estimating the support of a high-dimensional distribution显示文摘	Schlkopf B	2001	Neural Computation2001，13，7：	1
2	New support vector algorithms 显示文摘	Schlkopf B Smola A Williamson R and Bartlett PL	2000	Neural Computation2000，12，5：	1
3	Experimentally Optimal v in Support Vector Regression for Different Noise Models and Parameter Setting显示文摘	Chalimourda Schlkopf B Smola A	2004	Neural Networks2004，17，1：	1
4	A tutorial on support vector regression 显示文摘	Smola A J and Schlkopf B	2004	Statistics and Computing2004，14，19：	1
5	Input space versus feature space in kernel-based methods显示文摘	Schlkopf B Mika S Burgets C	1999	IEEE Trans on Neural Networks1999，10，5：	1
6	Input space versus feature space in kernel-based methods 显示文摘	SCHLKOPF B MIKA S BURGES C	1999	IEEE Trans Neural Networks1999，10，5：	1
7	Estimating the support of a high dimensional distribution显示文摘	SCHLKOPF B PLATT J C TAYLOR J S		0，，7：	1
8	A tutorial on support vector regression显示文摘	Smola A J Schlkopf B	2004	Statistics and Computing2004，14，3：	1
9	Input space versus feature space in kernel-based methods显示文摘	Schlkopf B Mika S Burges C	1999	IEEE Trans Neural Networks1999，10，5：	1
10	Nonlinear component analysis as a kernel eigenvalue problem显示文摘	SCHLKOPF B SMOLA A MULLER K R	1998	Neural Computation1998，10，5：	1
11	Input Space Versus Feature Space in Kernel-based Methods显示文摘	Schlkopf B Mika S Burges C	1999	IEEE Trans Neural Networks1999，10，5：	1
12	Input space versus feature space in kernel-based methods显示文摘	SCHLKOPF B MIKA S BURGES C	1999	IEEE Transaction Neural Networks1999，10，5：	1
13	Experimentally optimal v in support vector regression for different noise models and parameter setting显示文摘	CHALIMOURDA A SCHLKOPF B SMOI A A	2004	Neural Networks2004，17，1：	1
14	Estimating the Support of a High-dimensional Distribution显示文摘	Schlkopf B Platt J C Shawe-Taylor J	2001	Neural Computation2001，13，7：	1
15	Estimating the support of a high - dimensional distribution 显示文摘	Schlkopf B Platt JC Shawe-Taylor J L	2001	Neural Compat2001，13，7：	1
16	A tutorial on support vector re- gression显示文摘	Smola A J Schlkopf B	2004	Statistics and computing2004，14，3：	1
17	Nolinear Component Analysis as a Kernel Eigenvalue Problem显示文摘	Schlkopf B Smola A Müller K R	1998	Neural Computation1998，10，5：	1
18	A direct method for building sparse kernel learning algorithms显示文摘	SCHLKOPF B BAKIR G	2006	Journal of Machine Learning Research2006，7，4：	1
19	Atutorial on support vector regression显示文摘	Smola A J Schlkopf B	2004	Statistics and Computing2004，14，3：	1
20	Estimating the support of a high-dimensional distribution显示文摘	PLATT J SHAWE-TAYLOR J	2001	Neural Computation2001，13，7：	1

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