Sharma, Arun

Watanabe, Osamu

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Theoretical Computer Science 288 (2002) 195–196
www.elsevier.com/locate/tcs
Preface
The following six papers are the revised and expanded version of six of the 26 pa-
pers presented at the Tenth International Conference on Algorithmic Learning Theory
(ALT’99), held at Waseda University International Conference Center, Tokyo, Japan,
December 6–8, 1999. The conference proceedings appeared as Lecture Notes in Arti-
3cial Intelligence, vol. 1720. All the papers were subjected to the standard refereeing
process of this journal.
The 3eld of algorithmic learning theory studies theoretical aspects of machine learn-
ing and related topics. In order to formally study various learning mechanisms, many
learning frameworks have been proposed and studied. All six papers presented here
study properties of learning problems and=or learning algorithms under one or more of
these learning frameworks.
The 3rst paper by Balc7azar, Castro, Guijarro, and Simon investigates the query com-
plexity in the query learning framework. They introduce a new uniform combinatorial
characterization for investigating query complexity of learning problems. In query learn-
ing, a learning algorithm is expected to produce a hypothesis for an unknown concept
by making queries on the concept of certain types. The query complexity is simply the
number of queries necessary and su;cient for obtaining a correct hypothesis. Balc7azar
et al. succeeded in giving a uniform way of characterizing query complexity for some
important query types, thereby yielding a uniform perspective to query complexity
analysis.
The second and the third papers are about on-line learning. In the on-line learning
framework, the task of a learning algorithm is to make prediction about some unknown
concept based on examples given on-line (almost) as well as the best prediction ex-
pert. Takimoto and Warmuth investigate a problem of pruning planar directed acyclic
decision graphs which is a generalization of decision tree pruning. Here each pruning
is regarded as an expert, and the best expert is the pruning making the most accurate
prediction. They show e;cient on-line algorithms that can predict nearly as well as the
best pruning of a given decision graph. The paper by Goldman and Kwek considers the
problem of learning some subclasses of pattern languages. In general, learning pattern
languages is very di;cult, but due to its importance in various applications, this learn-
ing problem has been studied extensively in several learning frameworks. Here they
investigate on-line learning of pattern languages. By using relationships to “Winnow”,
a well-known on-line learning algorithm, they discuss the learnability=non-learnability
for some subclasses of pattern languages.
In the fourth paper, Bshouty, Eiron, and Kushilevitz introduce a variant of the PAC
learning framework, for studying and developing learning algorithms robust to noisy
0304-3975/02/$ - see front matter c© 2002 Elsevier Science B.V. All rights reserved.
PII: S0304 -3975(01)00399 -1
196 O. Watanabe, A. Sharma / Theoretical Computer Science 288 (2002) 195–196
data. In the PAC learning framework, a learning algorithm is given examples on some
unknown concept randomly drawn from the domain under some unknown distribution,
and the goal of the algorithm is to make an approximately good prediction hypothesis
with high probability. In the original PAC learning framework, it is assumed that given
examples are all correct w.r.t. the unknown concept. But for developing learning algo-
rithms robust to noisy data and investigating its di;culty, researchers have introduced
some variations of PAC learning. Bshouty et al. generalize the previous notions of
noisy data and introduce “nasty noise model” that assumes more general and much
worse noisy data. Both positive and negative results are presented.
The situation where given data may contain some errors is also studied in the paper
by Lange and Grieser for inductive inference algorithms. The task of these algorithms
is to come up with a predictive model about some unknown concept whose examples
are presented one element at a time. The learning algorithm is required to converge to
a predictive model that is always correct from some point onwards. This is essentially
the learning in the limit framework. In this setting, the learning algorithm, at any given
time, has access to all the data seen so far. If the learning algorithm is required to
make its conjectures based only on the current data and the previous conjecture, then
the framework is referred to as iterative learning or memory limited learning. For both
these frameworks, Lange and Grieser show how the di;culty of learning problems is
dependent on the type of given data, such as noise-free vs. noisy data and positive only
vs. both positive and negative data. The 3nal paper by Stephan and Zeugmann is also
about learning in the limit. They provide an extensive investigation of the learnability
of approximations of recursively enumerable (r.e.) sets of recursive functions. They
resolve an open question of Blum and Blum and considerably extend the nature of this
study by considering more modern learning frameworks like robust learning.
The editors wish to thank the members of the program committee of ALT ’99 for
helping to select the papers. The editors also wish to express their sincere gratitude to
all the reviewers for their excellent eHort.
Osamu Watanabea
Arun Sharmab
aDepartment of Mathematical and Computing Sciences
Tokyo Institute of Technology, Tokyo 152-8552, Japan
E-mail address: watanabe@is.titech.ac.jp
bSchool of Computer Science and Engineering
University of New South Wales Mexico
E-mail address: arun@cse.unsw.edu.au


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