444 research outputs found
Efficiently Learning Monotone Decision Trees with ID3
Since the Probably Approximately Correct learning model was introduced in 1984, there has been much effort in designing computationally efficient algorithms for learning Boolean functions from random examples drawn from a uniform distribution. In this paper, I take the ID3 information-gain-first classification algorithm and apply it to the task of learning monotone Boolean functions from examples that are uniformly distributed over {0,1}^n. I limited my scope to the class of monotone Boolean functions that can be represented as read-2 width-2 disjunctive normal form expressions. I modeled these functions as graphs and examined each type of connected component contained in these models, i.e. path graphs and cycle graphs. I determined the influence of the variables in the pieces of these graph models in order to understand how ID3 behaves when learning these functions. My findings show that ID3 will produce an optimal decision tree for this class of Boolean functions
Truth Table Minimization of Computational Models
Complexity theory offers a variety of concise computational models for
computing boolean functions - branching programs, circuits, decision trees and
ordered binary decision diagrams to name a few. A natural question that arises
in this context with respect to any such model is this:
Given a function f:{0,1}^n \to {0,1}, can we compute the optimal complexity
of computing f in the computational model in question? (according to some
desirable measure).
A critical issue regarding this question is how exactly is f given, since a
more elaborate description of f allows the algorithm to use more computational
resources. Among the possible representations are black-box access to f (such
as in computational learning theory), a representation of f in the desired
computational model or a representation of f in some other model. One might
conjecture that if f is given as its complete truth table (i.e., a list of f's
values on each of its 2^n possible inputs), the most elaborate description
conceivable, then any computational model can be efficiently computed, since
the algorithm computing it can run poly(2^n) time. Several recent studies show
that this is far from the truth - some models have efficient and simple
algorithms that yield the desired result, others are believed to be hard, and
for some models this problem remains open.
In this thesis we will discuss the computational complexity of this question
regarding several common types of computational models. We shall present
several new hardness results and efficient algorithms, as well as new proofs
and extensions for known theorems, for variants of decision trees, formulas and
branching programs
DNF Sparsification and a Faster Deterministic Counting Algorithm
Given a DNF formula on n variables, the two natural size measures are the
number of terms or size s(f), and the maximum width of a term w(f). It is
folklore that short DNF formulas can be made narrow. We prove a converse,
showing that narrow formulas can be sparsified. More precisely, any width w DNF
irrespective of its size can be -approximated by a width DNF with
at most terms.
We combine our sparsification result with the work of Luby and Velikovic to
give a faster deterministic algorithm for approximately counting the number of
satisfying solutions to a DNF. Given a formula on n variables with poly(n)
terms, we give a deterministic time algorithm
that computes an additive approximation to the fraction of
satisfying assignments of f for \epsilon = 1/\poly(\log n). The previous best
result due to Luby and Velickovic from nearly two decades ago had a run-time of
.Comment: To appear in the IEEE Conference on Computational Complexity, 201
Learning Unions of -Dimensional Rectangles
We consider the problem of learning unions of rectangles over the domain
, in the uniform distribution membership query learning setting, where
both b and n are "large". We obtain poly-time algorithms for the
following classes:
- poly-way Majority of -dimensional rectangles.
- Union of poly many -dimensional rectangles.
- poly-way Majority of poly-Or of disjoint
-dimensional rectangles.
Our main algorithmic tool is an extension of Jackson's boosting- and
Fourier-based Harmonic Sieve algorithm [Jackson 1997] to the domain ,
building on work of [Akavia, Goldwasser, Safra 2003]. Other ingredients used to
obtain the results stated above are techniques from exact learning [Beimel,
Kushilevitz 1998] and ideas from recent work on learning augmented
circuits [Jackson, Klivans, Servedio 2002] and on representing Boolean
functions as thresholds of parities [Klivans, Servedio 2001].Comment: 25 pages. Some corrections. Recipient of E. M. Gold award ALT 2006.
To appear in Journal of Theoretical Computer Scienc
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
Isomorphism testing of read-once functions and polynomials
In this paper, we study the isomorphism testing problem of formulas in
the Boolean and arithmetic settings. We show that isomorphism testing
of Boolean formulas in which a variable is read at most once (known as
read-once formulas) is complete for log-space. In contrast, we observe
that the problem becomes polynomial time equivalent to the graph
isomorphism problem, when the input formulas can be represented as OR
of two or more monotone read-once formulas. This classifies the
complexity of the problem in terms of the number of reads, as read-3
formula isomorphism problem is hard for coNP.
We address the polynomial isomorphism problem, a special case of
polynomial equivalence problem which in turn is important from a
cryptographic perspective[Patarin EUROCRYPT\u2796, and Kayal SODA\u2711]. As our main result, we propose a deterministic polynomial time
canonization scheme for polynomials computed by constant-free
read-once arithmetic formulas. In contrast, we show that when the
arithmetic formula is allowed to read a variable twice, this problem
is as hard as the graph isomorphism problem
Decision lists and related Boolean functions
AbstractWe consider Boolean functions represented by decision lists, and study their relationships to other classes of Boolean functions. It turns out that the elementary class of 1-decision lists has interesting relationships to independently defined classes such as disguised Horn functions, read-once functions, nested differences of concepts, threshold functions, and 2-monotonic functions. In particular, 1-decision lists coincide with fragments of the mentioned classes. We further investigate the recognition problem for this class, as well as the extension problem in the context of partially defined Boolean functions (pdBfs). We show that finding an extension of a given pdBf in the class of 1-decision lists is possible in linear time. This improves on previous results. Moreover, we present an algorithm for enumerating all such extensions with polynomial delay
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