恳求
发表于 2025-3-21 16:44:45
书目名称Computational Learning Theory影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0232576<br><br> <br><br>书目名称Computational Learning Theory读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0232576<br><br> <br><br>
reject
发表于 2025-3-21 23:24:34
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摘要记录
发表于 2025-3-22 02:17:30
A minimax lower bound for empirical quantizer design, empirically designed vector quantizer is at least . (..) away from the optimal distortion for some distribution on a bounded subset of .., where . is the number of i.i.d. data points that are used to train the empirical quantizer.
可用
发表于 2025-3-22 05:15:18
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Bone-Scan
发表于 2025-3-22 11:58:23
On learning branching programs and small depth circuits,We study the learnability of branching programs and small-depth circuits with modular and threshold gates in both the exact and PAC learning models with and without membership queries. Our results extend earlier works and exhibit further applications of . in learning theory.
无弹性
发表于 2025-3-22 14:29:11
Computational Learning Theory978-3-540-68431-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
无弹性
发表于 2025-3-22 19:35:48
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garrulous
发表于 2025-3-22 23:06:57
Spezielle Varianten der SEM-Analyse, empirically designed vector quantizer is at least . (..) away from the optimal distortion for some distribution on a bounded subset of .., where . is the number of i.i.d. data points that are used to train the empirical quantizer.
sed-rate
发表于 2025-3-23 05:24:09
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verdict
发表于 2025-3-23 05:51:14
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