**Abstract:**
In terms of their application areas, machine learning algorithms can
be grouped into two categories as classification learning and
continuous (real valued) function approximation learning
algorithms. The difference between these categories is in the target
values of the examples; while the in the former target values are
nominal, the latter assumes continuous target values. This project
proposal is about learning, from a set of examples, approximations of
such continuous functions that their mathematical model either do not
exits or unknown. Using such an approximation function, it is possible
to predict the values of the target function at new data points. Due
to its importance in many application areas, studies in this area have
begun by statisticians long before the development of machine learning
approaches.The first approaches to this problem, called as regression
by statisticians, were about to determine the parameters of a linear
function that fits the training examples as close as possible. In
recent years, studies in machine learning have developed
non-parametric approaches to the function approximation problem. The
knowledge representation technique based on feature projections and
the prediction scheme based on voting among feature predictions that
we have developed recently have proven to be very successful in
classification problems. In this project, the application of knowledge
representation based on generalizations of feature projections and the
voting based prediction to the problem of continuous function
approximation will be investigated. The approximation algorithms that
will be developed in the context of this project will be evaluated on
the standard data sets available at UCI-Repository and the statistical
data sets compiled by some governmental institutions.

**Keywords:**
Machine Learning, Feature Projections, Continous Functions

**Principal Investigator:**
H. Altay Guvenir, Ph.D.

**Investigator:**
Ilhan Uysal

**Investigator:**
Tolga Aydin, BSc.

**Duration:** February 1999 - August 2000.

**Sponsor:**
Scientific and Technical Research
Council of Turkey

**Grant No: 198E015**

**Budget:** 1,005,550,000 TL (USD 2,873 in Feb. 1999).