INTRODUCTORY ISSUES. Paradigmatic considerations on syntactic pattern recognition
Methodology of syntactic pattern recognition ; STRING-BASED MODELS. Pattern recognition based on regular and CF grammars
Enhanced string-based models for pattern recognition
Inference (induction) of string languages
Applications of string methods ; TREE-BASED MODELS. Pattern recognition based on tree languages
Inference (induction) of tree languages
Applications of tree methods ; GRAPH-BASED MODELS. Pattern analysis with graph grammars
Inference (induction) of graph languages
Applications of graph methods ; FUTURE OF SYNTACTIC PATTERN RECOGNITION. Summary of results and open problems
This unique compendium presents the major methods of recognition and learning used in syntactic pattern recognition from the 1960s till 2018. Each method is introduced firstly in a formal way. Then, it is explained with the help of examples and its algorithms are described in a pseudocode. The survey of the applications contains more than 1,000 sources published since the 1960s. The open problems in the field, the challenges and the determinants of the future development of syntactic pattern recognition are discussed.This must-have volume provides a good read and serves as an excellent source of reference materials for researchers, academics, and postgraduate students in the fields of pattern recognition, machine perception, computer vision and artificial intelligence. (source: Nielsen Book Data)
Book — 1 online resource (x, 321 pages) Digital: text file; PDF.
History of Artificial Intelligence.- Symbolic Artificial Intelligence.- Computational Intelligence.- Search Methods.- Evolutionary Computing.- Logic-Based Reasoning.- Structural Models of Knowledge Representation.- Syntactic Pattern Analysis.- Rule-Based Systems.- Pattern Recognition and Cluster Analysis.- Neural Networks.- Reasoning with Imperfect Knowledge.- Defining Vague Notions in Knowledge-Based Systems.- Cognitive Architectures.- Theories of Intelligence in Philosophy and Psychology.- Application Areas of AI Systems.- Prospects of Artificial Intelligence.
(source: Nielsen Book Data)
In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader. (source: Nielsen Book Data)