A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Neural Networks
A Strategy for SPN Detection Based on Biomimetic Pattern Recognition and Knowledge-Based Features
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
An approach for moving object recognition based on BPR and CI
Information Systems Frontiers
Video frame quality assessment using points calculation in high dimensional space
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Channel equalization based on two weights neural network
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
High-Dimensional space geometrical informatics and its applications to image restoration
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Research on multi-degree-of-freedom neurons with weighted graphs
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Towards adaptive classification of motor imagery EEG using biomimetic pattern recognition
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
CMMB image sequences measurement based on computation in high-dimension space
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
| Hi-index | 0.01 |
Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size.