Proceedings of ELM-2016
Jiuwen Cao · Erik Cambria · Amaury Lendasse · Yoan Miche · Chi Man Vong
Mey 2017 · Proceedings in Adaptation, Learning and Optimization Ibhuku elingu-9 · Springer
5.0star
1 isibuyekezoreport
I-Ebook
285
Amakhasi
reportIzilinganiso nezibuyekezo aziqinisekisiwe Funda Kabanzi
Mayelana nale ebook
This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016. This conference will provide 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 break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), 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. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large‐scale computing and artificial intelligence.
This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
Izilinganiso nezibuyekezo
5.0
1 isibuyekezo
Nikeza le ebook isilinganiso
Sitshele ukuthi ucabangani.
Ulwazi lokufunda
Amasmathifoni namathebulethi
Faka uhlelo lokusebenza lwe-Google Play Amabhuku lwe-Android ne-iPad/iPhone. Livunyelaniswa ngokuzenzakalela ne-akhawunti yakho liphinde likuvumele ukuthi ufunde uxhunywe ku-inthanethi noma ungaxhunyiwe noma ngabe ukuphi.
Amakhompyutha aphathekayo namakhompyutha
Ungalalela ama-audiobook athengwe ku-Google Play usebenzisa isiphequluli sewebhu sekhompuyutha yakho.
Ama-eReaders namanye amadivayisi
Ukuze ufunde kumadivayisi e-e-ink afana ne-Kobo eReaders, uzodinga ukudawuniloda ifayela futhi ulidlulisele kudivayisi yakho. Landela imiyalelo Yesikhungo Sosizo eningiliziwe ukuze udlulise amafayela kuma-eReader asekelwayo.
