11 research outputs found
A theory of cross-validation error
This paper presents a theory of error in cross-validation testing of algorithms for predicting
real-valued attributes. The theory justifies the claim that predicting real-valued
attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore,
the theory indicates precisely how these conflicting demands must be balanced, in
order to minimize cross-validation error. A general theory is presented, then it is
developed in detail for linear regression and instance-based learning
Theoretical analyses of cross-validation error and voting in instance-based learning
This paper begins with a general theory of error in cross-validation testing of algorithms
for supervised learning from examples. It is assumed that the examples are described by
attribute-value pairs, where the values are symbolic. Cross-validation requires a set of
training examples and a set of testing examples. The value of the attribute that is to be
predicted is known to the learner in the training set, but unknown in the testing set. The
theory demonstrates that cross-validation error has two components: error on the training
set (inaccuracy) and sensitivity to noise (instability).
This general theory is then applied to voting in instance-based learning. Given an
example in the testing set, a typical instance-based learning algorithm predicts the designated
attribute by voting among the k nearest neighbors (the k most similar examples) to
the testing example in the training set. Voting is intended to increase the stability (resistance
to noise) of instance-based learning, but a theoretical analysis shows that there are
circumstances in which voting can be destabilizing. The theory suggests ways to minimize
cross-validation error, by insuring that voting is stable and does not adversely affect
accuracy
A comparison of different approaches to target differentiation with sonar
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2001.Thesis (Ph.D.) -- Bilkent University, 2001.Includes bibliographical references leaves 180-197This study compares the performances of di erent classication schemes and fusion techniques
for target di erentiation and localization of commonly encountered features in indoor robot
environments using sonar sensing Di erentiation of such features is of interest for intelligent
systems in a variety of applications such as system control based on acoustic signal detection
and identication map building navigation obstacle avoidance and target tracking The
classication schemes employed include the target di erentiation algorithm developed by
Ayrulu and Barshan statistical pattern recognition techniques fuzzy c means clustering
algorithm and articial neural networks The fusion techniques used are Dempster Shafer
evidential reasoning and di erent voting schemes To solve the consistency problem arising in
simple ma jority voting di erent voting schemes including preference ordering and reliability
measures are proposed and veried experimentally To improve the performance of neural
network classiers di erent input signal representations two di erent training algorithms
and both modular and non modular network structures are considered The best classication
and localization scheme is found to be the neural network classier trained with the wavelet
transform of the sonar signals This method is applied to map building in mobile robot
environments Physically di erent sensors such as infrared sensors and structured light systems
besides sonar sensors are also considered to improve the performance in target classication
and localization.Ayrulu (Erdem), BirselPh.D