Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11)
IBOOKS dosya biçimi, işletim sistemlerinden birinde çalışan cihazlar (bilgisayarlar, telefonlar, tabletler vb.) İçin Springer yazarından Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11) gibi bir e-kitap biçimi olarak geliştirilmiştir. Apple - MacOS veya iOS. Diğer e-kitap dosyaları gibi, örneğin Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11), IBOOKS dosyaları yalnızca metin değil, aynı zamanda grafik ve video bilgileri ile kitap okumak için çok uygun olan üç boyutlu nesneler, sunumlar ve diğer veri türlerini de içerebilir Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11) . IBOOKS dosyaları, basit ve etkileşimli bir kullanıcı arabirimi sağlayarak çoklu dokunma hareketlerini destekledikleri için iBook çoklu dokunma dosyaları olarak bilinir. Bu tür dosyalar Apple'ın iBooks Author'ı kullanılarak kolayca oluşturulabilir. Bu, e-kitap geliştirmek ve yayınlamak için ücretsiz bir programdır. Bu durumda, büyük olasılıkla, IBOOKS uzantılı dosyalar yalnızca belirli bir ücret ödendikten sonra kullanılabilir hale gelecektir. Ancak Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11) tamamen ücretsizdir. IBOOKS dosyaları Apple tarafından Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11) indirebileceğiniz ePub 3 formatına göre geliştirilmiştir. Ancak, Apple bazı ek markalı "yongalar" ekledi. IBOOKS dosyası olarak kaydedilen Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11) kitabı, kullanıcıların tüm Apple cihazlarında indirip okuması için iTunes'a yayınlanabilir.
İngilizce LAP LAMBERT Academic Publishing Gale, U.S. Supreme Court Records HACHETTE LIVRE-BNF Fransızca ERWIN N GRISWOLD ROBERT H BORK Türkçe Additional Contributors Kolektif Independently published WADE H MCCREE İspanyolca Book on Demand Ltd. MDPI AG Rusça Almanca Springer
Proceedings of ELM 2018 (Proceedings in Adaptation, Learning and Optimization (11), Band 11)
| Yazar | Springer |
|---|---|
| İsbn 10 | 303023309X |
| İsbn 13 | 978-3030233099 |
| Yayın Evi | Springer |
| Boyutlar ve boyutlar | 15.6 x 1.88 x 23.39 cm |
| tarafından gönderildi | 15 Ağustos 2020 |
This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21–23, 2018. This conference provided a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental “learning particles” filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM.
