標題: Titlebook: Learning Theory and Kernel Machines; 16th Annual Conferen Bernhard Sch?lkopf,Manfred K. Warmuth Conference proceedings 2003 Springer-Verlag [打印本頁] 作者: 神像之光環(huán) 時間: 2025-3-21 18:26
書目名稱Learning Theory and Kernel Machines影響因子(影響力)
書目名稱Learning Theory and Kernel Machines影響因子(影響力)學科排名
書目名稱Learning Theory and Kernel Machines網絡公開度
書目名稱Learning Theory and Kernel Machines網絡公開度學科排名
書目名稱Learning Theory and Kernel Machines被引頻次
書目名稱Learning Theory and Kernel Machines被引頻次學科排名
書目名稱Learning Theory and Kernel Machines年度引用
書目名稱Learning Theory and Kernel Machines年度引用學科排名
書目名稱Learning Theory and Kernel Machines讀者反饋
書目名稱Learning Theory and Kernel Machines讀者反饋學科排名
作者: languor 時間: 2025-3-21 21:46 作者: 陶醉 時間: 2025-3-22 02:53
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.作者: 噴油井 時間: 2025-3-22 06:28 作者: 膽汁 時間: 2025-3-22 12:23 作者: enormous 時間: 2025-3-22 14:09
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.作者: 無目標 時間: 2025-3-22 19:05 作者: 臭名昭著 時間: 2025-3-22 22:57 作者: 擔心 時間: 2025-3-23 02:03 作者: Offset 時間: 2025-3-23 07:05
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作者: Archipelago 時間: 2025-3-23 13:39 作者: 樹木中 時間: 2025-3-23 15:50 作者: chemoprevention 時間: 2025-3-23 20:28 作者: 線 時間: 2025-3-23 23:33
Tutorial: Learning Topics in Game-Theoretic Decision Makinghighlight areas in which computational learning theory has played a role and could play a greater role in the future. Covered areas include recent representational and algorithmic advances, stochastic games and reinforcement learning, no regret algorithms, and the role of various equilibrium concepts.作者: 嚴厲批評 時間: 2025-3-24 05:16
Bernhard Sch?lkopf,Manfred K. WarmuthIncludes supplementary material: 作者: 不能強迫我 時間: 2025-3-24 09:31
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/l/image/582823.jpg作者: nephritis 時間: 2025-3-24 11:31
978-3-540-40720-1Springer-Verlag Berlin Heidelberg 2003作者: buoyant 時間: 2025-3-24 17:40
Learning Theory and Kernel Machines978-3-540-45167-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: ARENA 時間: 2025-3-24 20:59 作者: 灰心喪氣 時間: 2025-3-25 00:14 作者: 供過于求 時間: 2025-3-25 03:45
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