自治
发表于 2025-3-21 19:34:49
书目名称Structural, Syntactic, and Statistical Pattern Recognition影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0880090<br><br> <br><br>书目名称Structural, Syntactic, and Statistical Pattern Recognition读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0880090<br><br> <br><br>
COM
发表于 2025-3-21 22:29:18
http://reply.papertrans.cn/89/8801/880090/880090_2.png
GAVEL
发表于 2025-3-22 00:30:18
http://reply.papertrans.cn/89/8801/880090/880090_3.png
释放
发表于 2025-3-22 04:52:21
GriMa: A Grid Mining Algorithm for Bag-of-Grid-Based Classificationuld be beneficial to obtain more discriminative features. Experiments on different datasets show that our algorithm is efficient and that adding the structure may greatly help the image classification process.
烦扰
发表于 2025-3-22 09:03:21
http://reply.papertrans.cn/89/8801/880090/880090_5.png
dearth
发表于 2025-3-22 13:48:59
Correlation Network Evolution Using Mean Reversion Autoregressionobtain a more meaningful mean reversion term. We show experimentally that the dynamic network model can be used to recover detailed statistical properties of the original network data. More importantly, it also suggests that the model is effective in analyzing the predictability of stock correlation networks.
发电机
发表于 2025-3-22 18:07:51
http://reply.papertrans.cn/89/8801/880090/880090_7.png
slow-wave-sleep
发表于 2025-3-22 21:17:02
http://reply.papertrans.cn/89/8801/880090/880090_8.png
dendrites
发表于 2025-3-23 03:10:57
http://reply.papertrans.cn/89/8801/880090/880090_9.png
Hiatal-Hernia
发表于 2025-3-23 05:46:34
http://reply.papertrans.cn/89/8801/880090/880090_10.png