Download or read book Multiple Classifier Systems written by Nikunj C. Oza and published by Springer Science & Business Media. This book was released on 2005-06 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Workshop on Multiple Classifier Systems, MCS 2005, held in Seaside, CA, USA in June 2005. The 42 revised full papers presented were carefully reviewed and are organized in topical sections on boosting, combination methods, design of ensembles, performance analysis, and applications. They exemplify significant advances in the theory, algorithms, and applications of multiple classifier systems – bringing the different scientific communities together.
Download or read book Multiple Classifier Systems written by Terry Windeatt and published by Springer. This book was released on 2003-08-03 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: The refereed proceedings of the 4th International Workshop on Multiple Classifier Systems, MCS 2003, held in Guildford, UK in June 2003. The 40 revised full papers presented with one invited paper were carefully reviewed and selected for presentation. The papers are organized in topical sections on boosting, combination rules, multi-class methods, fusion schemes and architectures, neural network ensembles, ensemble strategies, and applications
Download or read book Multiple Classifier Systems written by Fabio Roli and published by Springer Science & Business Media. This book was released on 2004-06 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Multiple Classifier Systems, MCS 2004, held in Cagliari, Italy in June 2004. The 35 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on bagging and boosting, combination methods, design methods, performance analysis, and applications.
Download or read book Multiple Classifier Systems written by Friedhelm Schwenker and published by Springer. This book was released on 2015-06-02 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Workshop on Multiple Classifier Systems, MCS 2015, held in Günzburg, Germany, in June/July 2015. The 19 revised papers presented were carefully reviewed and selected from 25 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics. They are organized in topical sections on theory and algorithms and application and evaluation.
Download or read book Multiple Classifier Systems written by Neamat El Gayar and published by Springer Science & Business Media. This book was released on 2010-03-25 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 9th International Workshop on Multiple Classifier Systems, MCS 2010, held in Cairo, Egypt, in April 2010. The 31 papers presented were carefully reviewed and selected from 50 submissions. The contributions are organized into sessions dealing with classifier combination and classifier selection, diversity, bagging and boosting, combination of multiple kernels, and applications.
Download or read book Multiple Classifier Systems written by Fabio Roli and published by Springer. This book was released on 2003-08-02 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications.
Download or read book Multiple Classifier Systems written by Carlo Sansone and published by Springer Science & Business Media. This book was released on 2011-06-14 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Workshop on Multiple Classifier Systems, MCS 2011, held in Naples, Italy, in June 2011. The 36 revised papers presented together with two invited papers were carefully reviewed and selected from more than 50 submissions. The contributions are organized into sessions dealing with classifier ensembles; trees and forests; one-class classifiers; multiple kernels; classifier selection; sequential combination; ECOC; diversity; clustering; biometrics; and computer security.
Download or read book Multiple Classifier Systems written by Josef Kittler and published by Springer. This book was released on 2003-05-15 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.
Download or read book Multiple Classifier Systems written by Josef Kittler and published by Springer. This book was released on 2003-06-26 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Multiple Classifier Systems, MCS 2000, held in Cagliari, Italy in June 2000.The 33 revised full papers presented together with five invited papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on theoretical issues, multiple classifier fusion, bagging and boosting, design of multiple classifier systems, applications of multiple classifier systems, document analysis, and miscellaneous applications.
Download or read book Multiple Classifier Systems written by Jón Atli Benediktsson and published by Springer. This book was released on 2009-06-10 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2009, held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-established series of meetings providing an inter- tional forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks,pattern recognition,machine learning and stat- tics) concerned with this research topic. From more than 70 submissions, the Program Committee selected 54 papers to create an interesting scienti?c program. The special focus of MCS 2009 was on the application of multiple classi?er systems in remote sensing. This part- ular application uses multiple classi?ers for raw data fusion, feature level fusion and decision level fusion. In addition to the excellent regular submission in the technical program, outstanding contributions were made by invited speakers Melba Crawford from Purdue University and Zhi-Hua Zhou of Nanjing Univ- sity. Papers of these talks are included in these workshop proceedings. With the workshop’sapplicationfocusbeingonremotesensing,Prof.Crawford’sexpertise in the use of multiple classi?cation systems in this context made the discussions on this topic at MCS 2009 particularly fruitful.
Download or read book Multiple Classifier Systems written by Michal Haindl and published by Springer. This book was released on 2007-06-21 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. It covers kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.
Download or read book Multiple Classifier Systems written by Zhi-Hua Zhou and published by Springer. This book was released on 2013-04-16 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Workshop on Multiple Classifier Systems, MCS 2013, held in Nanjing, China, in May 2013. The 34 revised papers presented together with two invited papers were carefully reviewed and selected from 59 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics.
Download or read book Hybrid Methods In Pattern Recognition written by Horst Bunke and published by World Scientific. This book was released on 2002-05-22 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Ensemble Classification Methods with Applications in R written by Esteban Alfaro and published by John Wiley & Sons. This book was released on 2018-11-05 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential guide to two burgeoning topics in machine learning – classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. This important resource shows how ensemble classification has become an extension of the individual classifiers. The text puts the emphasis on two areas of machine learning: classification trees and ensemble learning. The authors explore ensemble classification methods’ basic characteristics and explain the types of problems that can emerge in its application. Written by a team of noted experts in the field, the text is divided into two main sections. The first section outlines the theoretical underpinnings of the topic and the second section is designed to include examples of practical applications. The book contains a wealth of illustrative cases of business failure prediction, zoology, ecology and others. This vital guide: Offers an important text that has been tested both in the classroom and at tutorials at conferences Contains authoritative information written by leading experts in the field Presents a comprehensive text that can be applied to courses in machine learning, data mining and artificial intelligence Combines in one volume two of the most intriguing topics in machine learning: ensemble learning and classification trees Written for researchers from many fields such as biostatistics, economics, environment, zoology, as well as students of data mining and machine learning, Ensemble Classification Methods with Applications in R puts the focus on two topics in machine learning: classification trees and ensemble learning.
Download or read book Multiple Classifier Systems written by and published by . This book was released on 2004 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multiple Classifier Systems written by Zhi-Hua Zhou and published by . This book was released on 2013-04-24 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multiple Classifier Systems written by Jón Atli Benediktsson and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, held in Reykjavik, Iceland, in June 2009. The 52 revised full papers presented together with 2 invited papers were carefully reviewed and selected from more than 70 initial submissions. The papers are organized in topical sections on ECOC boosting and bagging, MCS in remote sensing, unbalanced data and decision templates, stacked generalization and active learning, concept drift, missing values and random forest, SVM ensembles, fusion of graphics, concepts and categorical data, clustering, and finally theory, methods and applications of MCS.
