edited by Sankar K Pal (Indian Statistical Institute, Calcutta) & Amita Pal (Indian Statistical Institute, Calcutta)

This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource.

• Pattern Recognition: Evolution of Methodologies and Data Mining (A Pal & S K Pal)
• Adaptive Stochastic Algorithms for Pattern Classification (M A L Thathachar & P S Sastry)
• Shape in Images (K V Mardia)
• Decision Trees for Classification: A Review and Some New Results (R Kothari & M Dong)
• Syntactic Pattern Recognition (A K Majumder & A K Ray)
• Fuzzy Sets as a Logic Canvas for Pattern Recognition (W Pedrycz & N Pizzi)
• Neural Network Based Pattern Recognition (V David Sanchez A)
• Networks of Spiking Neurons in Data Mining (K Cios & D M Sala)
• Genetic Algorithms, Pattern Classification and Neural Networks Design (S Bandyopadhyay et al.)
• Rough Sets in Pattern Recognition (A Skowron & R Swiniarski)
• Automated Generation of Qualitative Representations of Complex Objects by Hybrid Soft-Computing Methods (E H Ruspini & I S Zwir)
• Writing Speed and Writing Sequence Invariant On-line Handwriting Recognition (S-H Cha & S N Srihari)
• Tongue Diagnosis Based on Biometric Pattern Recognition Technology (K Wang et al.)
• and other papers