Titlebook: Artificial Neural Networks and Machine Learning - ICANN 2011; 21st International C Timo Honkela,W?odzis?aw Duch,Samuel Kaski Conference pro

MIFF

書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011影響因子(影響力)




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011影響因子(影響力)學科排名




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011網絡公開度




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011網絡公開度學科排名




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011被引頻次




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011被引頻次學科排名




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011年度引用




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011年度引用學科排名




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011讀者反饋




書目名稱Artificial Neural Networks and Machine Learning - ICANN 2011讀者反饋學科排名

BUCK

Observational Learning Based on Models of Overlapping Pathways,ted during observation, if the agent is able to perform action association, i.e. relate its own actions with the ones of the demonstrator. In addition, by designing the model to activate the same neural codes during execution and observation, we show how the agent can accomplish observational learning of novel objects.

法律

頂點

A Comparison of the Electric Potential through the Membranes of Ganglion Neurons and Neuroblastoma s, represents a pathologic neuron. We numerically solved the non-linear Poisson-Boltzmann equation, by considering the densities of charges dissolved in an electrolytic solution and fixed on both glycocalyx and cytoplasmic proteins. We found important differences among the potential profiles of the two cells.

PANT

,Momentum Acceleration of Least–Squares Support Vector Machines,o combine the popular Sequential Minimal Optimization (SMO) method with a momentum strategy that manages to reduce the number of iterations required for convergence, while requiring little additional computational effort per iteration, especially in those situations where the standard SMO algorithm for LS–SVMs fails to obtain fast solutions.

Forsake

,Fast Support Vector Training by Newton’s Method,ental Cholesky factorization in calculating corrections. By computer experiments, we show that the proposed method is comparable to or faster than SMO (Sequential minimum optimization) using the second order information.

Engaged

Conference proceedings 2011 ICANN 2011, held in Espoo, Finland, in June 2011. .The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and

Endoscope

tattle

https://doi.org/10.1007/978-3-7091-8634-3dom receptive fields in the image space. These . (IRF-NN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.

corporate