Fusion Methods For Unsupervised Learning Ensembles
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Fusion Methods For Unsupervised Learning Ensembles
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Author : Bruno Baruque
language : en
Publisher: Springer
Release Date : 2010-11-18
Fusion Methods For Unsupervised Learning Ensembles written by Bruno Baruque and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-18 with Computers categories.
The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enhance the effectiveness of such networks greatly. This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results. Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms. The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm outperforms earlier map-fusion methods and the simpler versions of the algorithm with which it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.
Applications Of Supervised And Unsupervised Ensemble Methods
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Author : Oleg Okun
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-10-06
Applications Of Supervised And Unsupervised Ensemble Methods written by Oleg Okun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-06 with Computers categories.
Expanding upon presentations at last year’s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.
Hybrid Classifiers
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Author : Michal Wozniak
language : en
Publisher: Springer
Release Date : 2013-09-16
Hybrid Classifiers written by Michal Wozniak and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-16 with Technology & Engineering categories.
This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.
Artificial Intelligence Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2016-12-12
Artificial Intelligence Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
Ongoing advancements in modern technology have led to significant developments in artificial intelligence. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of artificial intelligence.
Advances In Self Organizing Maps And Learning Vector Quantization
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Author : Erzsébet Merényi
language : en
Publisher: Springer
Release Date : 2016-01-07
Advances In Self Organizing Maps And Learning Vector Quantization written by Erzsébet Merényi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-07 with Technology & Engineering categories.
This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.
Advances In Self Organizing Maps And Learning Vector Quantization
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Author : Thomas Villmann
language : en
Publisher: Springer
Release Date : 2014-06-10
Advances In Self Organizing Maps And Learning Vector Quantization written by Thomas Villmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-10 with Technology & Engineering categories.
The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification. This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks. Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods. All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.
Ensemble Machine Learning
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Author : Cha Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-17
Ensemble Machine Learning written by Cha Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-17 with Computers categories.
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
Biologically Inspired Techniques For Knowledge Discovery And Data Mining
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Author : Alam, Shafiq
language : en
Publisher: IGI Global
Release Date : 2014-05-31
Biologically Inspired Techniques For Knowledge Discovery And Data Mining written by Alam, Shafiq and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-31 with Computers categories.
Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques. Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.
Biological And Medical Data Analysis
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Author : Nicos Maglaveras
language : en
Publisher: Springer
Release Date : 2006-11-23
Biological And Medical Data Analysis written by Nicos Maglaveras and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-23 with Medical categories.
This book constitutes the refereed proceedings of the 7th International Symposium on Biological and Medical Data Analysis, ISBMDA 2006, held in Thessaloniki, Greece, December 2006. Coverage in this volume includes functional genomics, sequence analysis, biomedical models, information modeling, biomedical signal processing, biomedical image analysis, biomedical data analysis, as well as decision support systems and diagnostic tools.
Advances In Machine Learning And Data Mining For Astronomy
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Author : Michael J. Way
language : en
Publisher: CRC Press
Release Date : 2012-03-29
Advances In Machine Learning And Data Mining For Astronomy written by Michael J. Way and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-29 with Computers categories.
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.