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arXiv 1802.00233cs.DScs.LG
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最小高度决策树的多项式时间构造

On Polynomial time Constructions of Minimum Height Decision Tree

Technion, Haifa, Israel(以色列理工学院(Technion), 海法)

Nader H. Bshouty, Waseem Makhoul

摘要:

在本文中,我们研究了一个多项式时间的算法,对于一个输入$A\subseteq {B_m}$输出一个决策树的$A$的最小深度。这个问题有许多应用,包括计算机视觉,组测试,从成员查询和博弈论中精确学习。Arkin等人和Moshkov给出了一个多项式时间的$(\ln |A|)$ -近似算法(用于深度)。Dinur和Steurer关于集合覆盖的结果意味着这个问题不能用比率$(1-o(1))\cdot \ln |A|$近似,除非P=NP。Moskov提出了$A$扩展教学维度的组合测度$ETD(A)$。他表明$ETD(A)$是$A$的决策树深度的下界,然后给出了{ \it指数时间} $ETD(A)/\log(ETD(A))$ -近似算法。在本文中,我们进一步研究了$ETD(A)$测度和一个新的组合测度$DEN(A)$,我们称之为集合$A$的密度。我们表明,$DEN(A)\le ETD(A)+1$。然后我们给出两个结果。第一个结果是,$A$的决策树的深度的Moshkov的下限$ETD(A)$大于通过文献中使用的经典技术获得的界限。第二个结果是$A$的决策树的深度的多项式时间$(\ln 2) DEN(A)$ -近似(以及因此$(\ln 2) ETD(A)$ -近似)算法。我们还表明,一个更好的近似比意味着P=NP。然后,我们应用上述结果学习类的析取谓词成员查询。我们证明了这个类的$ETD$是有界的,从上面的程度$d$的哈斯图。然后,我们表明,Moshkov算法可以在多项式时间内运行,是$(d/\log d)$ -近似算法。当度为常数时,这给出了最优算法。例如,学习恒定维空间上的轴平行射线。

英文摘要:

In this paper we study a polynomial time algorithms that for an input $A\subseteq {B_m}$ outputs a decision tree for $A$ of minimum depth. This problem has many applications that include, to name a few, computer vision, group testing, exact learning from membership queries and game theory. Arkin et al. and Moshkov gave a polynomial time $(\ln |A|)$- approximation algorithm (for the depth). The result of Dinur and Steurer for set cover implies that this problem cannot be approximated with ratio $(1-o(1))\cdot \ln |A|$, unless P=NP. Moskov the combinatorial measure of extended teaching dimension of $A$, $ETD(A)$. He showed that $ETD(A)$ is a lower bound for the depth of the decision tree for $A$ and then gave an {\it exponential time} $ETD(A)/\log(ETD(A))$-approximation algorithm. In this paper we further study the $ETD(A)$ measure and a new combinatorial measure, $DEN(A)$, that we call the density of the set $A$. We show that $DEN(A)\le ETD(A)+1$. We then give two results. The first result is that the lower bound $ETD(A)$ of Moshkov for the depth of the decision tree for $A$ is greater than the bounds that are obtained by the classical technique used in the literature. The second result is a polynomial time $(\ln 2) DEN(A)$-approximation (and therefore $(\ln 2) ETD(A)$-approximation) algorithm for the depth of the decision tree of $A$. We also show that a better approximation ratio implies P=NP. We then apply the above results to learning the class of disjunctions of predicates from membership queries. We show that the $ETD$ of this class is bounded from above by the degree $d$ of its Hasse diagram. We then show that Moshkov algorithm can be run in polynomial time and is $(d/\log d)$-approximation algorithm. This gives optimal algorithms when the degree is constant. For example, learning axis parallel rays over constant dimension space.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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