formation
发表于 2025-3-21 18:33:05
书目名称Learning Theory影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0582820<br><br> <br><br>书目名称Learning Theory读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0582820<br><br> <br><br>
Cervical-Spine
发表于 2025-3-21 20:36:59
A Function Representation for Learning in Banach Spacesh spaces and show how this function representation naturally arises in that problem. Furthermore, we provide circumstances in which these representations are dense relative to the uniform norm and discuss how the parameters in such representations may be used to fit data.
刺穿
发表于 2025-3-22 03:59:17
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LASH
发表于 2025-3-22 05:54:26
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions the number of training examples and depends in a detailed way on the interaction between the process parameters and the taxonomy structure. Preliminary experiments on real-world data provide support to our theoretical results.
Limpid
发表于 2025-3-22 11:39:43
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AGONY
发表于 2025-3-22 16:22:46
Concentration Bounds for Unigrams Language Modelarning algorithm is its expected per-word log-loss. We show that the leave-one-out method can be used for estimating the log-loss of the unigrams model with a PAC error of approximately ., for any distribution..We also bound the log-loss a priori, as a function of various parameters of the distribution.
跑过
发表于 2025-3-22 18:49:46
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ineluctable
发表于 2025-3-23 00:30:34
Learning Classes of Probabilistic Automata stochastic languages. We show that a MA may generate a stochastic language that cannot be generated by a PFA, but we show also that it is undecidable whether a MA generates a stochastic language. Finally, we propose a learning algorithm for a subclass of PFA, called PRFA.
愤怒事实
发表于 2025-3-23 02:57:55
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connoisseur
发表于 2025-3-23 09:35:50
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