Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/sciencelive/np/RAA9LiLSQsNoX65rgKIuxdOamVkDoCCZqaIGJt8ZG9HAI/assertion>. }
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- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. type AIDA-Sentence assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q169950 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q2539 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q192776 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. obtainsSupportFrom j.jag.2024.104034 assertion.