Performance bounds on the splitting algorithm for binary testing . Let M denote the number of possible hypotheses and let N denote the number of possible questions, each of which divides the set of hypotheses into two groups. There is also an initial distribution 0 over hypotheses. M. R. Garey and R. L. Graham. Performance bounds on the splitting algorithm for binary testing .
Performance bounds on the splitting algorithm for binary testing .
The optimal trees were found using a novel graph coloring idea. We summarize the main observations from our experiments below. Where relevant, we briefly mention related work in the literature. The expected depth of greedily induced decision trees was consistently very close to the optimal. Garey, M. R., and Graham, R. L. 1974.