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Supervised inductive learning with Lotka–Volterra derived models

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Abstract

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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Authors and Affiliations

  1. Biology Department, Emory University, Atlanta, GA, 30329, USA

    Karen Hovsepian

  2. Management Department, New Mexico Tech, Socorro, NM, 87801, USA

    Peter Anselmo

  3. Computer Science Department, New Mexico Tech, Socorro, NM, 87801, USA

    Subhasish Mazumdar

  4. Taylor Lab, O. Wayne Rollins Research Center, 1510 Clifton Road NE, Room 2101, Atlanta, GA, 30322, USA

    Karen Hovsepian

Authors
  1. Karen Hovsepian
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  2. Peter Anselmo
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  3. Subhasish Mazumdar
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Correspondence to Karen Hovsepian.

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Hovsepian, K., Anselmo, P. & Mazumdar, S. Supervised inductive learning with Lotka–Volterra derived models. Knowl Inf Syst 26, 195–223 (2011). https://doi.org/10.1007/s10115-009-0280-5

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  • DOI: https://doi.org/10.1007/s10115-009-0280-5

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