This paper investigates the problem of regression model generation. A model
is a superposition of primitive functions. The model structure is described by
a weighted colored graph. Each graph vertex corresponds to some primitive
function. An edge assigns a superposition of two functions. The weight of an
edge equals the probability of superposition. To generate an optimal model one
has to reconstruct its structure from its graph adjacency matrix. The proposed
algorithm reconstructs the~minimum spanning tree from the~weighted colored
graph. This paper presents a novel solution based on the prize-collecting
Steiner tree algorithm. This algorithm is compared with its alternatives.

研究论文探讨回归模型生成的问题，模型是原始函数的叠加，模型结构通过加权着色图描述，每个图顶点对应于某个原始函数，边赋予两个函数的叠加，边的权重等于叠加的概率，为了生成一个最优模型，需要从图邻接矩阵中重构其结构，所提出的算法从加权着色图重构最小生成树，本文提出了一种基于奖励收集斯坦纳树算法的新颖解决方案，并与其替代方案进行比较。