神像之光环
发表于 2025-3-21 18:26:07
书目名称Learning Theory and Kernel Machines影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0582823<br><br> <br><br>书目名称Learning Theory and Kernel Machines读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0582823<br><br> <br><br>
languor
发表于 2025-3-21 21:46:26
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陶醉
发表于 2025-3-22 02:53:01
Sparse Kernel Partial Least Squares Regressionrithm. The resulting .-KPLS algorithm explicitly models centering and bias rather than using kernel centering. An .-insensitive loss function is used to produce sparse solutions in the dual space. The final regression function for the .-KPLS algorithm only requires a relatively small set of support vectors.
喷油井
发表于 2025-3-22 06:28:59
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胆汁
发表于 2025-3-22 12:23:58
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enormous
发表于 2025-3-22 14:09:58
Multiplicative Updates for Large Margin Classifierstiplicative updates used in machine learning. In this paper, we provide complete proofs of convergence for these updates and extend previous work to incorporate sum and box constraints in addition to nonnegativity.
无目标
发表于 2025-3-22 19:05:45
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臭名昭著
发表于 2025-3-22 22:57:55
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担心
发表于 2025-3-23 02:03:02
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Offset
发表于 2025-3-23 07:05:53
Simplified PAC-Bayesian Margin Boundsit-norm feature vectors. Unit-norm margin bounds have been proved previously using fat-shattering arguments and Rademacher complexity. Recently Langford and Shawe-Taylor proved a dimension-independent unit-norm margin bound using a relatively simple PAC-Bayesian argument. Unfortunately, the Langford