Titlebook: Artificial Neural Networks - ICANN 2010; 20th International C Konstantinos Diamantaras,Wlodek Duch,Lazaros S. Il Conference proceedings 201

粗野的整个

书目名称Artificial Neural Networks - ICANN 2010影响因子(影响力)




书目名称Artificial Neural Networks - ICANN 2010影响因子(影响力)学科排名




书目名称Artificial Neural Networks - ICANN 2010网络公开度




书目名称Artificial Neural Networks - ICANN 2010网络公开度学科排名




书目名称Artificial Neural Networks - ICANN 2010被引频次




书目名称Artificial Neural Networks - ICANN 2010被引频次学科排名




书目名称Artificial Neural Networks - ICANN 2010年度引用




书目名称Artificial Neural Networks - ICANN 2010年度引用学科排名




书目名称Artificial Neural Networks - ICANN 2010读者反馈




书目名称Artificial Neural Networks - ICANN 2010读者反馈学科排名

小教堂

H. R. Koelz,P. G. Lankisch,S. Müller-LissnerTri-Class SVMs. The proposed framework is applied to facial expressions recognition task. The results show that . can exploit effectively the independent views and the unlabeled data to improve the recognition accuracy of facial expressions.

placebo

大酒杯

expound

Electrical Circuits of Ordinary Capacitorsng algorithm which has been found to have numerous advantages over Evolution Strategies. Our empirical results confirm the promise of this approach, and we discuss how it can be scaled up to expert-level Go players.

evanescent

发誓放弃

调色板

Hidden Markov Model for Human Decision Process in a Partially Observable Environment), which incorporates inference of a hidden variable in the environment and switching between exploration and exploitation. Our HMM-based model well reproduced the human behaviors, suggesting the human subjects actually performed exploration and exploitation to effectively adapt to this uncertain environment.

BRAWL

Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Studyis again symbolically represented, incorporating both initial model and learned specification, as shown by our case study. The case study illustrates how the integration of methodologies and principles from distinct AI areas can be relevant to build robust intelligent systems.

SUE

Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradientsng algorithm which has been found to have numerous advantages over Evolution Strategies. Our empirical results confirm the promise of this approach, and we discuss how it can be scaled up to expert-level Go players.