Bilkent CEIS DEPT 1993 Tech Report abstracts

All technical reports files are in compressed (gzip) postscript format.
The file names are of the sort BU-CEIS-YYXX.ps.z 

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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9301.ps.z">BU-CEIS-9301</A><br>

<h3>TITLE: An Algorithm for Classification by Feature Partitioning<br>
AUTHOR(S): Izzet Sirin and H. Altay Guvenir</h3>

ABSTRACT: This paper presents a new methodology for learning from examples,
called Classification by Feature Partitioning (CFP), which is an
inductive, incremental and supervised learning method. 
Learning in CFP is accomplished by storing the objects separately in
each feature dimension as disjoint partitions of values.
A partition, which is initially a point in the feature dimension, is
expanded through generalization.
The CFP algorithm specializes a  partition by subdividing it into
sub-partitions. It is shown that the CFP algorithm has low sample
complexity and training complexity. 
CFP is also empirically evaluated in three different domains, and the
results are compared with Instance-based learning, Nested
Generalized Exemplars and Decision Tree techniques.

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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9302.ps.z">BU-CEIS-9302</A><br>

<h3>TITLE: A LEXICAL-FUNCTIONAL GRAMMAR FOR TURKISH (M. Sc. Thesis)<br>
AUTHOR(S): Zelal Gungordu</h3>

ABSTRACT:Natural language processing is a research area which is becoming increasingly
popular each day for both academic and commercial reasons. Syntactic parsing
underlies most of the applications in natural language processing. Although
there have been comprehensive studies of Turkish syntax from a linguistic
perspective, this is one of the first attempts for investigating it extensively
from a computational point of view. In this thesis, a lexical-functional grammar
for Turkish syntax is presented. Our current work deals with regular Turkish
sentences that are structurally simple or complex.

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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9303.ps.z">BU-CEIS-9303</A><br>

<h3>TITLE: AN ATN GRAMMAR FOR TURKISH (M. Sc. Thesis)<br>
AUTHOR(S): Coskun Demir</h3>

ABSTRACT: NA

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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9304.ps.z">BU-CEIS-9304</A><br>

<h3>TITLE: Two-level Description of Turkish Morphology<br>
AUTHOR(S): Kemal Oflazer</h3>

ABSTRACT: This paper describes a full two-level morphological
description  of Turkish
word structures.  The description has been implemented using the
PC-KIMMO environment and is based on a root word
lexicon of about 23,000 roots words. The phonetic rules of
contemporary Turkish (spoken in Turkey) have been encoded 
using \ref{lastrule} two-level rules while the morphotactics  of the agglutinative
word structures  have been encoded as  finite-state machines for
verbal,  nominal paradigms and other categories. Almost all the special cases of, and
exceptions to phonological and morphological rules  have been
taken into account. In this paper, we describe the rules  and the finite state
machines along with examples and  a discussion of how various special cases were
handled. We also  describe some known limitations and problems with
this description. 

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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9305.ps.z">BU-CEIS-9305</A><br>

<h3>TITLE: Genetic Synthesis of Unsupervised Learning Algorithms<br>
AUTHOR(S): Ali Dasdan and Kemal Oflazer</h3>

ABSTRACT: This paper presents new unsupervised learning algorithms that have been synthesized using a genetic approach.  A set of such learning algorithms
has been compared with the classical Kohonen's Algorithm on the
Self-Organizing Map and has been found to provide a better performance measure.
This study indicates that there exist many unsupervised learning algorithms
that lead to an organization similar to that of Kohonen's Algorithm, and
that genetic algorithms can be used to search for optimal algorithms and
optimal architectures for the unsupervised learning.

<br><hr>
<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9306.ps.z">BU-CEIS-9306</A><br>

<h3>TITLE: A Genetic Algorithm for Classification by Feature 
       Partitioning<br>
AUTHOR(S): H. Altay Guvenir and Izzet Sirin</h3>

ABSTRACT: In this paper we describe a method for hybridizing a 
genetic algorithm and a feature partitioning (FP) classification 
algorithm. Learning in FP is accomplished by storing the objects 
separately in each feature dimension as partitions of the set of 
values. A partition is expanded through generalization, which is 
limited by a generalization limit. Prediction in FP is done 
through a voting among the class predictions of each feature. 
The effect of the prediction of a feature in the voting is
proportional with the weight of that feature. Feature 
partitioning method is implemented in the CFP (Classification
by Feature Partitioning) algorithm. We use the genetic algorithm 
and a training data set to learn real-valued weights and 
generalization limits associated with each feature in that 
domain. The experimental results indicate that the genetic 
algorithm can be used to learn the domain dependent parameters 
of a feature partitioning classifier, and the resulting hybrid 
system can be used to create compact representations with very 
good predictive accuracy.

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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9308.ps.z">BU-CEIS-9308</A><br>

<h3>TITLE: Complexity of the CFP, a Method for Classification Based <br>
       on Feature Partitioning<br>
AUTHOR(S):H. Altay Guvenir and Izzet Sirin </h3>

ABSTRACT: This paper presents a new methodology for learning 
from examples, called Classification by Feature Partitioning 
(CFP). Learning in CFP is accomplished by storing the objects 
separately in each feature dimension as disjoint partitions of 
values. A partition is expanded through generalization or 
specialized by subdividing it into sub-partitions. It is shown 
that the CFP algorithm has a low sample and training complexity. 

<br><hr>

<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9310.ps.z">BU-CEIS-9310</A><br>

<h3>TITLE: EMPIRICAL EVALUATION OF THE CFP ALGORITHM<br>
AUTHOR(S): Izzet Sirin and H. Altay Guvenir</h3>

ABSTRACT: This paper presents a new methodology for concept 
learning from examples, called Classification by Feature 
Partitioning (CFP), which is an inductive, incremental and 
supervised learning method. Learning in CFP is accomplished by 
storing the objects separately in each feature dimension as 
disjoint partitions of values. A partition is expanded through 
generalization or specialized by dividing it into subpartitions.
An empirical evaluation of the CFP on real-world data sets is 
presented.

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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9311.ps.z">BU-CEIS-9311</A><br>

<h3>TITLE: Parallel Classification by Feature Partitioning<br>
AUTHOR(S): Huseyin Simitci and H. Altay Guvenir</h3>

ABSTRACT: This work presents a parallel method for learning from examples using 
parallel feature partitioning (PFP). Feature
partitioning (FP) is an inductive, incremental and supervised learning
method proposed by Sirin and Guvenir. PFP assigns
feature dimensions to separate nodes. Learning in PFP is accomplished
by storing the objects separately in each feature dimension as disjoint
partitions of values. Every node expands a partition, which is initially
a point in the feature dimension through generalization. 
The CFP algorithm specializes a  partition by subdividing it into
sub-partitions.

PFP is implemented in the PCFP (Parallel Classification by Feature 
Partitioning)
algorithm. PCFP is tested in six different domains, and results are
compared with CFP of Sirin and Guvenir.
<br><hr>

<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9312.ps.z">BU-CEIS-9312</A><br>
<h3>
TITLE: LEARNING WITH FEATURE PARTITIONING (M. Sc. Thesis)<br>
AUTHOR(S): Izzet Sirin
</h3>
ABSTRACT:
 This report presents a new methodology of learning
from examples, based on {\em feature partitioning}
{\em Classification by Feature Partitioning} (CFP) is a
particular implementation of this technique, which is an
inductive, incremental, and supervised learning method.
Learning in CFP is accomplished by storing the objects separately in
each feature dimension as disjoint partitions of values.
A partition, a basic unit of representation
which is initially a point in the feature dimension, is
expanded through generalization.
The CFP algorithm specializes a  partition by subdividing it into
two subpartitions.
Theoretical (with respect to PAC--model)
and empirical evaluation of the CFP is presented and
compared with some other similar techniques.
 
Keywords: Machine learning, inductive learning,
incremental learning, supervised learning, feature partitioning.


<br><hr>
<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9313.ps.z">BU-CEIS-9313 </A><br>
<h3>
TITLE:  Design and implementation of a spelling checker for Turkish. <br>
AUTHOR(S): Aysin Solak and Kemal Oflazer
</h3>

ABSTRACT: This paper presents the design and implementation of a
spelling checker for Turkish. Turkish is an agglutinative language
in which words are formed  by affixing a sequence  of morphemes to a
root word.
Parsing agglutinative word structures
has attracted relatively little attention except for applications
areas for general purpose morphological processors.
Parsing words in such languages even for spelling checking purposes requires substantial morphological
and  morphophonemic analysis techniques, and spelling correction (not
addressed in this paper) is significantly more complicated. In this paper, we
present the design and implementation of a morphological
root-driven parser for Turkish word structures 
which has been incorporated into  a spelling checking kernel for on-line
Turkish  text.
The agglutinative nature of the language 
complex word formations, various phonetic harmony rules, and subtle
exceptions  present certain difficulties not usually encountered in
the spelling checking  of languages like English and make this a very
challenging problem.
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<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9314.ps.z">BU-CEIS-9314</A><br>

<h3>TITLE: HETEROGENEOUS INFERENCE IN DESIGN<br>
AUTHOR(S): Varol Akman
</h3>

ABSTRACT: For those of us involved in the attempt to construct formal
models and environments in which the world of design can be subjected
to scientific experimentation, the raison d'etre of logic has been
rather well-understood.  The aim of this position paper is not really
to challenge this view but rather to complement and extend
it. Specifically, we discuss why heterogeneous inference - inference
that proceeds from information represented in more than one form - is
crucial.

<br><hr>
<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9315.ps.z">BU-CEIS-9315</A><br>

<h3>TITLE: COMPUTATIONAL SITUATION THEORY<br>
AUTHOR(S): Erkan Tin and Varol Akman</h3>

ABSTRACT: Situation theory has been developed over the last decade and
various versions of the theory have been applied to a number of
linguistic issues.  However, not much work has been done in regard to
its computational aspects.  In this paper, a medium (called BABY-SIT)
which uses situation-theoretic constructs as its computational
foundation is proposed. The features of this experimental environment
are compared to those of the existing approaches towards
`computational situation theory.'

<br><hr>
<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9316.ps.z">BU-CEIS-9316</A><br>

<h3>TITLE: HYPERSOLVER: A GRAPH-BASED TOOL FOR MODELING WITH SETS<br>
AUTHOR(S): Varol Akman and Mujdat Pakkan</h3>

ABSTRACT: This paper investigates an alternative set theory (due to
Peter Aczel) which uses a graphical representation for sets and
thereby allows the representation of non-well-founded sets.  A
program, called HYPERSOLVER, which can solve systems of equations
defined in terms of sets in the universe of this new theory is
presented.

<br><hr>
<br><A href="http://www.cs.bilkent.edu.tr/tech-reports/1993/BU-CEIS-9317.ps.z">BU-CEIS-9317</A><br></A><br>

<h3>
TITLE:       Artificial Minds: Fact or Fantasy?<br>
AUTHOR(s):   David Davenport</h3>

Abstract: Trying to fathom the inner workings of the human mind has
been one of the main preoccupation's of philosophy and psychology. The
advent of the electronic digital computer provided both a new tool and
a new perspective for this quest, and spawned the research field known
today as artificial intelligence (A.I.). Yet, is an artificial
intelligence a real possibility, or is it simply a myth, unreachable
in principle?  Would an A.I. be conscious, would it literally be an
artificial mind, or is there something intensely human about minds
which preclude our constructing one? Despite years of research, we
still have no real answer to these most basic of questions.
Speculation and controversy abound, rekindled of late by the
resurgence of the connectionist paradigm. This paper examines some of
the key arguments in the debate and suggests a new theory based on
"inscriptors" may offer plausible solutions. 

Keywords: Artificial Intelligence, Chinese room experiment, Turing
test, symbol grounding, Inscriptors. 

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