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Grammatical inference, the main topic of this book, is a scientific area that lies at the intersection of multiple fields. Researchers from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory, and many others have their own contribution. Therefore, it is not surprising that the topic has also a few other names such as grammar learning, automata inference, grammar identification, or grammar induction. To simplify the location of present contribution, we can divide all books relevant to grammatical inference into three groups: theoretical, practical, and applicable. In greater part this book is practical, though one can also find the elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus some reference to real-life problems.
The purpose of this book is to present old and modern methods of grammatical inference from the perspective of practitioners. To this end, the Python programming language has been chosen as the way of presenting all the methods. Included listings can be directly used by the paste-and-copy manner to other programs, thus students, academic researchers, and programmers should find this book as the valuable source of ready recipes and as an inspiration for their further development.
State Merging Algorithms
Partition-Based Algorithms
Substring-Based Algorithms
Identification Using Mathematical Modeling
A Decomposition-Based Algorithm
An Algorithm Based on a Directed Acyclic Word Graph
Applications of GI Methods in Selected Fields
A: A Quick Introduction to Python
B: Python’s Tools for Automata, Networks, Genetic Algorithms, and SAT Solving
C: OML and its Usage in IronPython