Product Details
Combining Pattern Classifiers: Methods and Algorithms
Free Shipping+Easy returns
Product Details
Pattern Recognition: Introduction, Features, Classifiers and Principles (De Gruyter Textbook)
Free Shipping+Easy returns
Product Details
Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
Free Shipping+Easy returns
Product Details
Statistical and Neural Classifiers: An Integrated Approach to Design (Advances in Computer Vision and Pattern Recognition)
Free Shipping+Easy returns
Product Details
A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
Free Shipping+Easy returns
Product Details
Multiple Classifier Systems: 10th International Workshop, MCS 2011, Naples, Italy, June 15-17, 2011. Proceedings (Lecture …
Free Shipping+Easy returns
Product Details
Multiple Classifier Systems: 12th International Workshop, MCS 2015, Günzburg, Germany, June 29 – July 1, 2015, Proceedings…
Free Shipping+Easy returns
Product Details
Multiple Classifier Systems: 11th International Workshop, MCS 2013, Nanj
ing, China, May 15-17, 2013. Proceedings (Lecture …
Free Shipping+Easy returns
Product Details
A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
Free Shipping+Easy returns
Product Details
Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (L…
Free Shipping+Easy returns
Product Details
Computational Intelligence for Pattern Recognition (Studies in Computational Intelligence Book 777)
Free Shipping+Easy returns
Product Details
Learning Resources Pattern Block Design Cards, Color Recognition, STEM Toy, Ages 4+
Free Shipping+Easy returns
Обложка
About Learning Kernel Classifiers An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Products
Recently classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. There are numbers of different Decision Support System (DSS) that has developed to operate on the minimum input data set or the output data set to give the correct decision. A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the face recognition performance. In this book, a face recognition system has been developed by applying multi-classifier fusion on the output of the three different classification methods namely Artificial Neural Network, Genetic Algorithm and Euclidean distance measure based on the Principal Component Analysis dimensionality reduction technique. Experimental results and performance analysis show the comparison results between multi-classifier fusion based face recognition system with individual classifier performance. Face Recognition Using Multiple Classifier Fusion (Paperback)
Products
Proceedings of the NATO Advanced Research Workshop on Syntactic and Structural Pattern Recognition, held in Barcelona-Sitges, Spain, October 23-25, 1986 Thirty years ago pattern recognition was dominated by the learning machine concept: that one could automate the process of going from the raw data to a classifier. The derivation of numerical features from the input image was not considered an important step. One could present all possible features to a program which in turn could find which ones would be useful for pattern recognition. In spite of significant improvements in statistical inference techniques, progress was slow. It became clear that feature derivation was a very complex process that could not be automated and that features could be symbolic as well as numerical. Furthennore the spatial relationship amongst features might be important. It appeared that pattern recognition might resemble language analysis since features could play the role of symbols strung together to form a word. This led. to the genesis of syntactic pattern recognit
ion, pioneered in the middle and late 1960’s by Russel Kirsch, Robert Ledley, Nararimhan, and Allan Shaw. However the thorough investigation of the area was left to King-Sun Fu and his students who, until his untimely death, produced most of the significant papers in this area. One of these papers (syntactic recognition of fingerprints) received the distinction of being selected as the best paper published that year in the IEEE Transaction on Computers. Therefore syntactic pattern recognition has a long history of active research and has been used in industrial applications. • ISBN:9783642834646 • Format:Paperback • Publication Date:2011-12-14
Products
A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes: Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering. Product DetailsISBN-13: 9781118315231 Publisher: Wiley Publication Date: 09-15-2014 Pages: 384 Product Dimensions: 6.20(w) x 9.40(h) x 1.20(d)About the Author Ludmila Kuncheva is a Professor of Computer Science at Bangor University, United Kingdom. She has received two IEEE Best Paper awards. In 2012, Dr. Kuncheva was awarded a Fellowship to the International Association for Pattern Recognition (IAPR) for her contributions to multiple classifier systems.Read an Excerpt Click to read or download
Products
Chapter1: Dimensionality Reduction Techniques.- Chapter2: Linear classifier techniques.- Chapter3: Regression techniques. Chapter4: Probabilistic supervised classifier and unsupervised clustering.- Chapter5: Computational intelligence.- Chapter6: Statistical test in pattern recognition. • Author: E S Gopi • ISBN:9783030222727 • Format:Hardcover • Publication Date:2019-10-28
Products
This book provides comprehensive coverage of information theory elements implied in modern CVPR algorithms. It introduces information theory to researchers in CVPR, and additionally introduces interesting CVPR problems to information theorists. Introduction Interest Points, Edges and Contour Grouping Contour and Region Based Image Segmentation Registration, Matching, and Recognition Image and Pattern Clustering Feature Selection and Transformation Classifier Design • Author: Francisco Escolano Ruiz,Pablo Suau P\\u0026eacute;rez,Boy\\u0026aacute;n Ivanov Bonev • ISBN:9781848822962 • Format:Hardcover • Publication Date:2009-07-31
Products
Part I Computational Intelligence in Visual Pattern Recognition.- 1 Visual Pattern Recognition.- 2 Computational Intelligence Techniques.- 3 Multi-Feature Pattern Recognition.- Part II Feature Selection and Classification.- 4 Fuzzy-Rough Discriminative Feature Selection and Classification.- 5 Hand Posture and Face Recognition using Fuzzy-Rough Approach.- 6 Boosting based Fuzzy-Rough Pattern Classifier.- Part III Biologically Inspired Approaches in Hand Posture Recognition.- 7 Hand Posture Recognition using Neurobiologically Inspired Features.- 8 Attention based Segmentation and Recognition (ASR) Algorithm for Hand Postures Against Complex Backgrounds.- Appendices.- Index. • Author: Pramod Kumar Pisharady,Prahlad Vadakkepat,Loh Ai Poh • ISBN:9789812870551 • Format:Hardcover • Publication Date:2014-06-25
