2,582 research outputs found
Top-Down Induction of Decision Trees: Rigorous Guarantees and Inherent Limitations
Consider the following heuristic for building a decision tree for a function
. Place the most influential variable of
at the root, and recurse on the subfunctions and on the
left and right subtrees respectively; terminate once the tree is an
-approximation of . We analyze the quality of this heuristic,
obtaining near-matching upper and lower bounds:
Upper bound: For every with decision tree size and every
, this heuristic builds a decision tree of size
at most .
Lower bound: For every and , there is an with decision tree size such that
this heuristic builds a decision tree of size .
We also obtain upper and lower bounds for monotone functions:
and
respectively. The lower bound disproves conjectures of Fiat and Pechyony (2004)
and Lee (2009).
Our upper bounds yield new algorithms for properly learning decision trees
under the uniform distribution. We show that these algorithms---which are
motivated by widely employed and empirically successful top-down decision tree
learning heuristics such as ID3, C4.5, and CART---achieve provable guarantees
that compare favorably with those of the current fastest algorithm (Ehrenfeucht
and Haussler, 1989). Our lower bounds shed new light on the limitations of
these heuristics.
Finally, we revisit the classic work of Ehrenfeucht and Haussler. We extend
it to give the first uniform-distribution proper learning algorithm that
achieves polynomial sample and memory complexity, while matching its
state-of-the-art quasipolynomial runtime
Epistemic virtues, metavirtues, and computational complexity
I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium
An Overview of Schema Theory
The purpose of this paper is to give an introduction to the field of Schema
Theory written by a mathematician and for mathematicians. In particular, we
endeavor to to highlight areas of the field which might be of interest to a
mathematician, to point out some related open problems, and to suggest some
large-scale projects. Schema theory seeks to give a theoretical justification
for the efficacy of the field of genetic algorithms, so readers who have
studied genetic algorithms stand to gain the most from this paper. However,
nothing beyond basic probability theory is assumed of the reader, and for this
reason we write in a fairly informal style.
Because the mathematics behind the theorems in schema theory is relatively
elementary, we focus more on the motivation and philosophy. Many of these
results have been proven elsewhere, so this paper is designed to serve a
primarily expository role. We attempt to cast known results in a new light,
which makes the suggested future directions natural. This involves devoting a
substantial amount of time to the history of the field.
We hope that this exposition will entice some mathematicians to do research
in this area, that it will serve as a road map for researchers new to the
field, and that it will help explain how schema theory developed. Furthermore,
we hope that the results collected in this document will serve as a useful
reference. Finally, as far as the author knows, the questions raised in the
final section are new.Comment: 27 pages. Originally written in 2009 and hosted on my website, I've
decided to put it on the arXiv as a more permanent home. The paper is
primarily expository, so I don't really know where to submit it, but perhaps
one day I will find an appropriate journa
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
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