Titlebook: Learning Theory and Kernel Machines; 16th Annual Conferen Bernhard Sch?lkopf,Manfred K. Warmuth Conference proceedings 2003 Springer-Verlag

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書(shū)目名稱(chēng)Learning Theory and Kernel Machines影響因子(影響力)




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書(shū)目名稱(chēng)Learning Theory and Kernel Machines網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Learning Theory and Kernel Machines被引頻次




書(shū)目名稱(chēng)Learning Theory and Kernel Machines被引頻次學(xué)科排名




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書(shū)目名稱(chēng)Learning Theory and Kernel Machines讀者反饋




書(shū)目名稱(chēng)Learning Theory and Kernel Machines讀者反饋學(xué)科排名

languor

陶醉

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.

噴油井

膽汁

enormous

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.

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臭名昭著

擔(dān)心

Offset

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