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Titlebook: Knowledge Management and Acquisition for Intelligent Systems; 16th Pacific Rim Kno Kouzou Ohara,Quan Bai Conference proceedings 2019 Spring

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樓主: lexicographer
11#
發(fā)表于 2025-3-23 12:12:54 | 只看該作者
Empirical Evaluation of Deep Learning-Based Travel Time Prediction,accuracy can be offered for alleviating traffic congestion. In addition, to eliminate the influence of nonlinear external factors, a variety of extrinsic data with abrupt properties will be acquired in real time and become part of the research considerations.
12#
發(fā)表于 2025-3-23 14:20:24 | 只看該作者
13#
發(fā)表于 2025-3-23 21:59:31 | 只看該作者
14#
發(fā)表于 2025-3-24 01:26:18 | 只看該作者
15#
發(fā)表于 2025-3-24 04:30:46 | 只看該作者
16#
發(fā)表于 2025-3-24 08:58:34 | 只看該作者
Neurofeedback and AI for Analyzing Child Temperament and Attention Levels,evels. The experimental results not only infer that the value of temperament with EEG classification could be consistent, but also provide a valid way to classify attention levels in specific time period. The combination of the parental subjective report with EEG data demonstrates a novel and valuab
17#
發(fā)表于 2025-3-24 14:15:29 | 只看該作者
Constructing Dataset Based on Concept Hierarchy for Evaluating Word Vectors Learned from Multisensehierarchies of WordNet and BabelNet to evaluate the similarity between word vectors. We empirically show that the proposed dataset and evaluation metric allow us to evaluate word vectors for multisense words more properly than metrics for an existing dataset.
18#
發(fā)表于 2025-3-24 17:36:44 | 只看該作者
,Prior-Knowledge-Embedded LDA with Word2vec – for Detecting Specific Topics in Documents,f the conventional LDA. (2) Words in each sentence are annotated such that the annotations reflect the topic of the sentence. The conventional LDA sometimes makes confusing/mixed annotations to the words in a single sentence. Our approach, on the contrary, can make annotations that reflect the topic
19#
發(fā)表于 2025-3-24 19:10:01 | 只看該作者
20#
發(fā)表于 2025-3-25 01:14:10 | 只看該作者
Study on Influencers of Cryptocurrency Follow-Network on GitHub,orithm in identifying influencers of a specific domain. (2) The rate of contribution of a user correlates with their rate of influence, but the explanatory power is small. The amount of activity on GitHub is not as essential for OSS influencers as it is on Twitter, which requires a lot of activity t
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