The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago. A large number of different approaches have been proposed. Hybrid methods aim at combining the advantages of different paradigms within a single system.Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and subsymbolic learning, and others. Also included is recent work on multiple classifier systems. Furthermore, the book deals with applications in on-line and off-line handwriting recognition, remotely sensed image interpretation, fingerprint identification, and automatic text categorization.Contents:Neuro-Fuzzy Systems:Fuzzification of Neural Networks for Classification Problems (H Ishibuchi & M Nii)Neural Networks for Structural Pattern Recognition:Adaptive Graphic Pattern Recognition: Foundations and Perspectives (G Adorni et al.)Adaptive Self-Organizing Map in the Graph Domain (S Günter & H Bunke)Clustering for Hybrid Systems:From Numbers to Information Granules: A Study in Unsupervised Learning and Feature Analysis (A Bargiela & W Pedrycz)Combining Neural Networks and Hidden Markov Models:Combination of Hidden Markov Models and Neural Networks for Hybrid Statistical Pattern Recognition (G Rigoll)From Character to Sentences: A Hybrid Neuro-Markovian System for On-Line Handwriting Recognition (T Artières et al.)Multiple Classifier Systems:Multiple Classifier Combination: Lessons and Next Steps (T K Ho)Design of Multiple Classifier Systems (F Roli & G Giacinto)Fusing Neural Networks Through Fuzzy Integration (A Verikas et al.)Applications of Hybrid Systems:Hybrid Data Mining Methods in Image Processing (A Klose & R Kruse)Robust Fingerprint Identification Based on Hybrid Pattern Recognition Methods (D-W Jung & R-H Park)Text Categorization Using Learned Document Features (M Junker et al.)Readership: Graduate students, lecturers and researchers in computer science, computer engineering, electrical engineering and related fields.Key Features:This book is unique in being a treatise on the statistical analysis of network traffic dataThe contributors are leading researches in the field and will give authoritative descriptions of cutting edge methodologyThe book features material from diverse areas, and as such forms a unified view of network cyber security