Supervised inductive learning with Lotka-Volterra derived models

作     者：Hovsepian, Karen Anselmo, Peter Mazumdar, Subhasish 

作者机构：O Wayne Rollins Res Ctr Taylor Lab Atlanta GA 30322 USA Emory Univ Dept Biol Atlanta GA 30329 USA New Mexico Inst Min & Technol Dept Management Socorro NM 87801 USA New Mexico Inst Min & Technol Dept Comp Sci Socorro NM 87801 USA 

出 版 物：《KNOWLEDGE AND INFORMATION SYSTEMS》 (知识和信息系统季刊)

年 卷 期：2011年第26卷第2期

页      面：195-223页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0701[理学-数学] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Institute of Complex Additive Systems Analysis  Socorro  NM 

主　　题：Supervised machine learning Classification Lotka-Volterra model Data mining algorithm Generalization theory 

摘      要：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.