Pattern Recognition: From Classical to Modern ApproachesThis 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. Contents: 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. Readership: Graduate students, researchers and academics in pattern recognition. |
Contents
Chapter | 1 |
1 | 9 |
Chapter 2 | 32 |
1 | 38 |
Chapter 3 | 75 |
5 | 104 |
Chapter 4 | 133 |
Chapter 5 | 147 |
Chapter 10 | 281 |
67 | 294 |
PATTERN CLASSIFICATION BASED ON QUANTUM | 301 |
93 | 319 |
Chapter 12 | 329 |
Chapter 13 | 347 |
103 | 379 |
Chapter 14 | 385 |
Other editions - View all
Pattern Recognition: From Classical to Modern Approaches Sankar K. Pal,Amita Pal Limited preview - 2001 |
Pattern Recognition: From Classical To Modern Approaches Sankar Kumar Pal,Amita Pal Limited preview - 2001 |
Common terms and phrases
action probability applications approach approximation attributes automata automaton Bayes Bayesian birds Boolean chromosomes class label classifier clustering consider convergence corresponding data mining data set decision tree defined denoted density discriminant function efficiency error rate estimation example feature space feature vector FFNN finite fuzzy integral fuzzy sets genetic algorithms given grammar hyperplanes IEEE Transactions inference input language layer learning automata linear discriminant look-ahead Machine Learning Mardia Markov chain matrix maximum membership methods minimal mixture neural networks neuro-fuzzy neurons node splitting objects optimal output parameters partition pattern class Petri net posterior probability distribution problem random regular grammar regular language represent rough set S. K. Pal selection Skowron soft computing Statistical stochastic string structure subset supervision syntactic pattern recognition techniques template training samples training set unsupervised learning updating values variable VGA-classifier weights wind profiler