Supervised Inductive Learning with Lotka-Volterra Derived Models

We present a classification algorithm built on our adaptation of the Generalized Lotka-Volterra model, well-known in mathematical ecology. The training algorithm itself consists only of computing several scalars, per each training vector, using a single global user parameter and then solving a linear system of equations. Construction of the system matrix is driven by our model and based on kernel functions. The model allows an interesting point of view of kernels´ role in the inductive learning process. We describe the model through axiomatic postulates. Finally, we present the results of the preliminary validation experiments.