Generalization of Predicates with String Arguments

I certify that I have read this thesis and that in my opinion it is fully adequate, in scope and in quality, as a thesis for the degree of Master of Science. I certify that I have read this thesis and that in my opinion it is fully adequate, in scope and in quality, as a thesis for the degree of Master of Science. I certify that I have read this thesis and that in my opinion it is fully adequate, in scope and in quality, as a thesis for the degree of Master of Science. I certify that I have read this thesis and that in my opinion it is fully adequate, in scope and in quality, as a thesis for the degree of Master of Science. String/sequence generalization is used in many different areas such as machine learning, example-based machine translation and DNA sequence alignment. In this thesis, a method is proposed to find the generalizations of the predicates with string arguments from the given examples. Trying to learn from examples is a very hard problem in machine learning, since finding the global optimal point to stop generalization is a difficult and time consuming process. All the work done until now is about employing a heuristic to find the best solution. This work is one of them. In this study, some restrictions applied by the SLGG (Specific Least General Generalization) algorithm, which is developed to be used in an example-based machine translation system, are relaxed to find the all possible alignments of two strings. Moreover, a Euclidian distance like scoring mechanism is used to find the most specific generalizations. Some of the generated templates are eliminated by four different selection/filtering approaches to get a good solution set. Finally, the result set is presented as a decision list, which provides the handling of exceptional cases. Acknowledgement I would like to express my deepest gratitude to Asst. Prof. İlyas Çiçekli for his supervision, guidance, suggestions and invaluable encouragement throughout the development of this thesis. I would like to thank to committee members for reading this thesis and their comments. I would like to thank to all my friends for their encouragement and logistic support. I have to thank my boss Dr. Semih Çetin, head of cyberSoft and Dr. Mesut Göktepe, project manager for their continuous support during my MS studies. I would like to thank to my parents, my grandmother …