Name
Abstract | ||
|---|---|---|
Neural computation in Clifford algebras, which include familiar complex numbers and quaternions as special cases, has recently become an active research field. As always, neurons are the atoms of computation. The paper provides a general notion for the Hessian matrix of Clifford neurons of an arbitrary algebra. This new result on the dynamics of Clifford neurons then allows the computation of optimal learning rates. A thorough discussion of error surfaces together with simulation results for different neurons is also provided. The presented contents should give rise to very efficient second-order training methods for Clifford Multilayer perceptrons in the future. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74690-4_88 | ICANN (1) |
Keywords | Field | DocType |
active research field,hessian matrix,familiar complex number,neural computation,clifford neuron,clifford algebra,different neuron,efficient second-order training method,arbitrary algebra,clifford multilayer perceptrons,multi layer perceptron,second order | Computer science,Quaternion,Hessian matrix,Models of neural computation,Artificial intelligence,Computation,Clifford algebra,Complex number,Algebra,Optimal learning,Pure mathematics,Perceptron,Machine learning | Conference |
Volume | ISSN | ISBN |
4668 | 0302-9743 | 3-540-74689-7 |
Citations | PageRank | References |
2 | 0.48 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sven Buchholz | 1 | 3 | 0.84 |
| Kanta Tachibana | 2 | 12 | 4.81 |
| Eckhard M. S. Hitzer | 3 | 8 | 1.61 |