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.

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