By Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu

ISBN-10: 3319466801

ISBN-13: 9783319466804

ISBN-10: 331946681X

ISBN-13: 9783319466811

The 4 quantity set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues the court cases of the twenty third overseas convention on Neural details Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 complete papers offered have been conscientiously reviewed and chosen from 431 submissions. The four volumes are equipped in topical sections on deep and reinforcement studying; giant info research; neural info research; robotics and regulate; bio-inspired/energy effective details processing; entire mind structure; neurodynamics; bioinformatics; biomedical engineering; information mining and cybersecurity workshop; computing device studying; neuromorphic undefined; sensory conception; development reputation; social networks; brain-machine interface; laptop imaginative and prescient; time sequence research; data-driven technique for extracting latent good points; topological and graph established clustering equipment; computational intelligence; info mining; deep neural networks; computational and cognitive neurosciences; concept and algorithms.

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Additional info for Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV

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However, we have figured out that the trained features with ImageNet pre-trained model are not suitable in the case of medical image problem as it can be seen in Table 3. Using our method depicted in Table 2, the single phase architecture performed around 5 % better than the conventional network architectures. It is interesting to see that single network with 5-min-only input data has slightly better performance than the other phases. It can be seen that 5-min data has more significant features compared to the others.

LNCS, vol. 9431, pp. 295–306. Springer, Heidelberg (2016). 1007/978-3-319-29451-3_24 11. : Extracting player’s stance information from 3D motion data: a case study in tennis groundstrokes. , et al. ) PSIVT 2015 Workshops. LNCS, vol. 9555, pp. 307– 318. Springer, Heidelberg (2016). 1007/978-3-319-30285-0_25 12. : The implementation and evaluation of a methodical approach to qualitative sports skill analysis instruction. J. Teach. Phys. Educ. kr Abstract. Deep learning has been a growing trend in various fields of natural image classification as it performs state-of-the-art result on several challenging tasks.

Regarding the validity of the exact classification of start and stop of event there are possible uncertainties in regards to expert’s decision boundaries. However, it must be noted that the expectation is that, even with missing distal marker trajectories, tennis professionals should be able to identify the frame(s) with the most likely intended impact with the ball utilising the interactive functions of the developed animated 3D stick figure player [5]. 4 GHz, and a solid-state disk. The data set was considered similar to Microsoft’s Kinect 2 in terms of occasional loss of marker time series information and relatively low sampling frequency.

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Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV by Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu


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