Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing write by Nicolino John Pizzi. This book was released on 1997. Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing available in PDF, EPUB and Kindle.
Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing
Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing write by Nicolino John Pizzi. This book was released on 1997. Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing available in PDF, EPUB and Kindle.
Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing
Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing write by . This book was released on 1907. Pattern Recognition Using Robust Discrimination and Fuzzy Set Theoretic Preprocessing available in PDF, EPUB and Kindle. Classification is the empirical process of creating a mapping from individual patterns to a set of classes and its subsequent use in predicting the classes to which new patterns belong. Tremendous energies have been expended in developing systems for the creation of the mapping component. Less effort has been devoted to the nature and analysis of the data component, namely, strategies that transform the data in order to simplify, in some sense, the classification process. The purpose of this thesis is to redress somewhat this imbalance by introducing two novel preprocessing methodologies. Fuzzy interruptible encoding determines the respective degrees to which a feature belongs to a collection of fuzzy sets and subsequently using these membership grades in place of the original feature. Burnishing tarnished gold standards compensates for the possible imprecision of a well-established reference test by adjusting, if necessary, the class labels in the design set while maintaining the test's vital discriminatory power. The methodologies were applied to several synthetic data sets as well as biomedical spectra acquired from magnetic resonance and infrared spectrometers. Both fuzzy encoding and burnishing consistently improved the discriminatory power of the underlying classifiers. They are insensitive to outliers and often reduce the training time for iterative classifiers such as the multi-layer perceptron. With the latter, reclassification only occurs for data within the design set; outliers within the test set are flagged but not altered. Therefore, the accepted gold standard is left in a pristine state sullied only by its original tarnish.
Pattern Recognition And Big Data
Pattern Recognition And Big Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Pattern Recognition And Big Data write by Sankar Kumar Pal. This book was released on 2016-12-15. Pattern Recognition And Big Data available in PDF, EPUB and Kindle. Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications.Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.
Pattern Recognition
Pattern Recognition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Pattern Recognition write by Sankar K. Pal. This book was released on 2001. Pattern Recognition available in PDF, EPUB and Kindle. 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. 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.
