Binary And Multiclass Classification
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Binary And Multiclass Classification
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Author : Brian Kolo
language : en
Publisher: Lulu.com
Release Date : 2011-02
Binary And Multiclass Classification written by Brian Kolo and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02 with Science categories.
Classifiers are 'black boxes' that examine an input and place the input into a category. Examples include medical diagnostics (presence/absence of a disease), quality control (is a part defective?), search engine results (does a page match the search criteria?), language identification (what is the language of a document?), and data quality metrics (does this data set have sufficient quality metrics?). Increasingly, software tools are developed that implement or automate the classification process. Competing classification algorithms are compared to determine which is better suited for a particular application. This book develops the tools needed to measure classifier performance, compare classifiers, and rank order the results. This text examines the properties of binary and multiclass classifiers from a general perspective. Topics include classifier performance metrics, error analysis, comparison of classifier performance, metric distributions, and receiver operator characteristics (ROC).
Ensemble Learning Algorithms With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2021-04-26
Ensemble Learning Algorithms With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-26 with Computers categories.
Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.
Generation And Termination Of Binary Decision Trees For Nonparametric Multiclass Classification
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Author : Saul Brian Gelfand
language : en
Publisher:
Release Date : 1984
Generation And Termination Of Binary Decision Trees For Nonparametric Multiclass Classification written by Saul Brian Gelfand and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Algorithms categories.
A two-step procedure for nonparametric multiclass classifier design is described. A multiclass recursive partioning algorithm is given which generated a single binary decision tree for classifying all classes. The algorithm minimizes the Bayes risk at each node. A tree termination algorithm is given which optimally terminates binary decision trees. The algorithm yields the unique tree with fewest nodes which minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively.
Machine Learning And Knowledge Discovery In Databases
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Author : Walter Daelemans
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-04
Machine Learning And Knowledge Discovery In Databases written by Walter Daelemans 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 2008-09-04 with Computers categories.
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Multi Class Classification With Machine Learning And Fusion
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Author : Cristina Garcia Cifuentes
language : en
Publisher:
Release Date : 2009
Multi Class Classification With Machine Learning And Fusion written by Cristina Garcia Cifuentes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Multi-class classification is the core issue of many pattern recognition tasks. Several applications require high-end machine learning solutions to provide satisfying results in operational contexts. However, most efficient ones, like SVM or Boosting, are generally mono-class, which introduces the problem of translating a global multi-class problem is several binary problems, while still being able to provide at the end an answer to the original multi-class issue. Present work aims at providing a solution to this multi-class problematic, by introducing a complete framework with a strong probabilistic and structured basis. It includes the study of error correcting output codes correlated with the definition of an optimal subdivision of the multi-class issue in several binary problems, in a complete automatic way. Machine learning algorithms are studied and benchmarked to facilitate and justify the final selection. Coupling of automatically calibrated classifiers output is obtained by applying iterative constrained regularisations, and a logical temporal fusion is applied on temporal-redundant data (like tracked vehicles) to enhance performances. Finally, ranking scores are computed to optimize precision and recall is ranking-based systems. Each step of the previously described system has been analysed from a theoretical an empirical point of view and new contributions are introduced, so as to obtain a complete mathematically coherent framework which is both generic and easy-to-use, as the learning procedure is almost completely automatic. On top of that, quantitative evaluations on two completely different datasets have assessed both the exactitude of previous assertions and the improvements that were achieved compared to previous methods.
Algorithms And Analysis For Multi Category Classification
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Author : Dav Arthur Zimak
language : en
Publisher:
Release Date : 2006
Algorithms And Analysis For Multi Category Classification written by Dav Arthur Zimak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
Machine Learning Mastery With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-04-08
Machine Learning Mastery With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-08 with Computers categories.
The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this Ebook, learn exactly how to get started and apply machine learning using the Python ecosystem.
Neurocomputing
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Author : Francoise Fogelman Soulie
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neurocomputing written by Francoise Fogelman Soulie 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 2012-12-06 with Computers categories.
This volume contains the collected papers of the NATO Conference on Neurocomputing, held in Les Arcs in February 1989. For many of us, this conference was reminiscent of another NATO Conference, in 1985, on Disordered Systems [1], which was the first conference on neural nets to be held in France. To some of the participants that conference opened, in a way, the field of neurocomputing (somewhat exotic at that time!) and also allowed for many future fruitful contacts. Since then, the field of neurocomputing has very much evolved and its audience has increased so widely that meetings in the US have often gathered more than 2000 participants. However, the NATO workshops have a distinct atmosphere of free discussions and time for exchange, and so, in 1988, we decided to go for another session. This was an ~casion for me and some of the early birds of the 1985 conference to realize how much, and how little too, the field had matured.
Machine Learning From Weak Supervision
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Author : Masashi Sugiyama
language : en
Publisher: MIT Press
Release Date : 2022-08-23
Machine Learning From Weak Supervision written by Masashi Sugiyama and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-23 with Mathematics categories.
Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom. The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation.
Big Data Analytics And Knowledge Discovery
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Author : Sanjay Madria
language : en
Publisher: Springer
Release Date : 2015-08-09
Big Data Analytics And Knowledge Discovery written by Sanjay Madria and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-09 with Computers categories.
This book constitutes the refereed proceedings of the 17th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2015, held in Valencia, Spain, September 2015. The 31 revised full papers presented were carefully reviewed and selected from 90 submissions. The papers are organized in topical sections similarity measure and clustering; data mining; social computing; heterogeneos networks and data; data warehouses; stream processing; applications of big data analysis; and big data.