Multivariate analysis

Ordinal classification refers to an important category of real world problems,
in which the attributes of the instances to be classified and the classes are
linearly ordered. Many applications of machine learning frequently involve
situations exhibiting an order among the different categories represented by
the class attribute. In ordinal classification the class value is converted into a
numeric quantity and regression algorithms are applied to the transformed
data. The data is later translated back into a discrete class value in a postprocessing
step. This thesis is devoted to an empirical study of ordinal and
non-ordinal classification algorithms for intrusion detection in WLANs. We
used ordinal classification in conjunction with nine classifiers for the
experiments in this thesis. All classifiers are parts of the WEKA machinelearning
workbench. The results indicate that most of the classifiers give
similar or better results with ordinal classification compared to non-ordinal
classification.

Through a small contained environment, this study evaluates an information-based complexity metric theory and its relationship to the effort expended in constructing a program. The metric, which calculates the amount of information present in a program specification, determines the specification's complexity measure. The observed measures of programmer effort were the numbers of keystrokes, insertions, deletions, and runs needed to complete the program specification. It was theorized that a program with a higher complexity value than that of another program will require more programmer resources to complete. A significant relationship between the metric and the number of keystrokes was found.

Two algorithms for greatest common factor (GCF) extraction
from two multivariable polynomials, based on
generalized Pade approximation, are presented. The reduced
transfer matrices for two-dimensional (20) systems are
derived from two 20 state-space models. Tests for product
and sum separabilities of multivariable functions are also
given.

Improving the quality of a product and manufacturing processes at a low cost is an economic and technological challenge which quality engineers and researches must contend with. In general, the quality of products and their cost are the main concerns for manufactures. This is because improving quality is very crucial for staying competitive and improving the organization's market position. However, some difficulty of finding where the standard of good quality is still remains. Customers' satisfaction is a key for setting up the quality target. One possible solution is to develop control limits, which are the limits for indicating the area of nonconforming product on the basis of minimizing the total cost or loss to the customer as well as to the manufacturer. Therefore, the goal of this dissertation is to develop an effective tool to improve a high quality of product while maintaining a minimum cost.