Computer Science – Neural and Evolutionary Computing
Scientific paper
2007-07-26
Computer Science
Neural and Evolutionary Computing
12 pages, 2 figures, 1 table
Scientific paper
In this paper we embed $m$-dimensional Euclidean space in the geometric algebra $Cl_m $ to extend the operators of incidence in ${R^m}$ to operators of incidence in the geometric algebra to generalize the notion of separator to a decision boundary hyperconic in the Clifford algebra of hyperconic sections denoted as ${Cl}({Co}_{2})$. This allows us to extend the concept of a linear perceptron or the spherical perceptron in conformal geometry and introduce the more general conic perceptron, namely the {elliptical perceptron}. Using Clifford duality a vector orthogonal to the decision boundary hyperplane is determined. Experimental results are shown in 2-dimensional Euclidean space where we separate data that are naturally separated by some typical plane conic separators by this procedure. This procedure is more general in the sense that it is independent of the dimension of the input data and hence we can speak of the hyperconic elliptic perceptron.
Nieto Isidro B.
Vallejo Refugio J.
No associations
LandOfFree
Clifford Algebra of the Vector Space of Conics for decision boundary Hyperplanes in m-Euclidean Space does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Clifford Algebra of the Vector Space of Conics for decision boundary Hyperplanes in m-Euclidean Space, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Clifford Algebra of the Vector Space of Conics for decision boundary Hyperplanes in m-Euclidean Space will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-41086