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Titlebook: Machine Intelligence and Big Data in Industry; Dominik Ry?ko,Piotr Gawrysiak,Henryk Rybiński Book 2016 Springer International Publishing A

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樓主: risky-drinking
21#
發(fā)表于 2025-3-25 04:31:41 | 只看該作者
22#
發(fā)表于 2025-3-25 08:57:36 | 只看該作者
2197-6503 ded versions of presentations delivered at the Industrial Se.This book presents valuable contributions devoted topractical applications of Machine Intelligence and Big Data in various branchesof the industry. All the contributions are extended versions of presentationsdelivered at the Industrial Ses
23#
發(fā)表于 2025-3-25 12:42:53 | 只看該作者
Automatic Sentiment Analysis in Polish Languagem using previously prepared data set and (d) the final evaluation using 10 fold cross validation. We show that this method provides good results and can be used to simplify sentiment analysis processes for Polish language content.
24#
發(fā)表于 2025-3-25 16:40:34 | 只看該作者
Learning Curve with Machine Translation Based on Parallel, Bilingual Corporaarticle describes consecutive steps of the project, i.e. collecting data and creating parallel, bilingual corpora, checking open source ready-made solutions and the novel, effective . solution that has been proposed. The final chapter summarizes the solution, together with the results based on . metrics.
25#
發(fā)表于 2025-3-25 22:29:16 | 只看該作者
Semantic Textual Similarity Using Various Approaches-poor approaches to some compact and easy applied knowledge-rich methods (using structured knowledge base frameworks like WordNet, Wikipedia or BabelNet). The evaluation of the proposed methods was performed using the datasets from SemEval-2014/15 tasks.
26#
發(fā)表于 2025-3-26 03:12:11 | 只看該作者
Automatic Translation of Multi-word Labelscal layers of domain ontologies. In the method we take advantage of Wikipedia and dictionaries services available on the Internet in order to provide translations of thematic texts from a given area of interest. Experimental evaluation shows usefulness of the proposed method in translating specialized thematic dictionaries.
27#
發(fā)表于 2025-3-26 06:49:38 | 只看該作者
28#
發(fā)表于 2025-3-26 10:48:59 | 只看該作者
Application of Machine Learning Algorithms for Bitcoin Automated Tradingd that Bitcoin market is still in its youth, and further market opportunities can be found. To the best of our knowledge, this is the first work that tries to investigate applying machine learning methods for the purpose of creating trading strategies on the Bitcoin market.
29#
發(fā)表于 2025-3-26 13:44:36 | 只看該作者
30#
發(fā)表于 2025-3-26 18:35:05 | 只看該作者
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