Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Making large-scale support vector machine learning practical
Advances in kernel methods
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Applying LSTM to Time Series Predictable through Time-Window Approaches
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A Discrete Probabilistic Memory Model for Discovering Dependencies in Time
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Text classification using string kernels
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Journal of Machine Learning Research
Diffusion of context and credit information in Markovian models
Journal of Artificial Intelligence Research
Evolino: hybrid neuroevolution / optimal linear search for sequence learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
An efficient classification approach for large-scale mobile ubiquitous computing
Information Sciences: an International Journal
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This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.