Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Kanta TACHIBANA"'
Autor:
Kensei Takeda, Kanta Tachibana
Publikováno v:
Journal of the Robotics Society of Japan. 40:519-527
Publikováno v:
International Symposium on Affective Science and Engineering. :1-3
Publikováno v:
Information and Control Systems. :2-11
Deep Learning (DL) plays an important role in machine learning and artificial intelligence. DL is widely applied in many fields with high dimensional data, including natural language processing, image recognition. High dimensional data can lead to pr
Publikováno v:
Advances in Applied Clifford Algebras. 32
Publikováno v:
PROCEEDINGS OF THE 14TH NATIONAL CONFERENCE ON FUNDAMENTAL AND APPLIED INFORMATION TECHNOLOGY RESEARCH.
Autor:
Kanta Tachibana, Yuta Takagi
Publikováno v:
International Symposium on Affective Science and Engineering. :1-4
Autor:
Kanta Tachibana, Kentaro Otsuka
Publikováno v:
2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).
The complex neural network expands on the complex plane more than the real neural network taking the real axis. So its figure transformation ability and learning speed are very good. However, the neural network needs to determine nonlinear transforma
Autor:
Kanta Tachibana, Minh Tuan Pham
Publikováno v:
Advances in Applied Clifford Algebras. 26:1013-1032
Clustering is one of the most useful methods for understanding similarity among data. However, most conventional clustering methods do not pay sufficient attention to the geometric distributions of data. Geometric algebra (GA) is a generalization of
Autor:
Ryuta Fukazawa, Kanta Tachibana
Publikováno v:
Robotic Sailing 2016 ISBN: 9783319454528
A robot sailor can obtain its behaviour autonomously with reinforcement learning. However, reinforcement learning suffers from the curse of dimensionality, with an increase in state variables and an exponential increase in the number of states to rea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b77d26cf46043523ce358e9d8a18ac03
https://doi.org/10.1007/978-3-319-45453-5_7
https://doi.org/10.1007/978-3-319-45453-5_7
Publikováno v:
IJCNN
This paper discusses feature extraction methods. The feature extraction methods such principal component analysis and multiple discriminant analysis are very important techniques in machine learning research areas. The characteristic of feature extra