http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>.
Introduction -- 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 -- References.
Artificial Intelligence (incl. Robotics)
Document Preparation and Text Processing
Studies in Computational Intelligence, 673