Multilabel Classification

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Multilabel Classification

Author : Francisco Herrera
Publisher : Springer
ISBN 13 : 331941111X
Total Pages : 200 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Multilabel Classification by : Francisco Herrera

Download or read book Multilabel Classification written by Francisco Herrera and published by Springer. This book was released on 2016-08-09 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

Machine Learning and Knowledge Discovery in Databases

Author : Wray Buntine
Publisher : Springer Science & Business Media
ISBN 13 : 3642041736
Total Pages : 787 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Wray Buntine

Download or read book Machine Learning and Knowledge Discovery in Databases written by Wray Buntine and published by Springer Science & Business Media. This book was released on 2009-09-03 with total page 787 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper 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.

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Author : Rana, Dipti P.
Publisher : IGI Global
ISBN 13 : 1799873730
Total Pages : 309 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance by : Rana, Dipti P.

Download or read book Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance written by Rana, Dipti P. and published by IGI Global. This book was released on 2021-06-04 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Semantic Processing of Legal Texts

Author : Enrico Francesconi
Publisher : Springer
ISBN 13 : 3642128378
Total Pages : 255 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Semantic Processing of Legal Texts by : Enrico Francesconi

Download or read book Semantic Processing of Legal Texts written by Enrico Francesconi and published by Springer. This book was released on 2010-05-10 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen much new research on the interface between artificial intelligence and law, looking at issues such as automated legal reasoning. This collection of papers represents the state of the art in this fascinating and highly topical field.

Machine Learning and Knowledge Discovery in Databases

Author : Walter Daelemans
Publisher : Springer Science & Business Media
ISBN 13 : 354087478X
Total Pages : 714 pages
Book Rating : 4.5/5 (48 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Walter Daelemans

Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence: Theories, Models and Applications

Author :
Publisher :
ISBN 13 : 9788354087885
Total Pages : 0 pages
Book Rating : 4.0/5 (878 download)

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Book Synopsis Artificial Intelligence: Theories, Models and Applications by :

Download or read book Artificial Intelligence: Theories, Models and Applications written by and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th Hellenic Conference on Artificial Intelligence, SETN 2008, held at Syros, Greece in October 2008. The 27 revised full papers together with 17 revised short papers were carefully reviewed and selected from 76 submissions. The papers address any area of artificial intelligence; particular fields of interest include: Adaptive Systems, AI and Creativity, AI rchitectures, Artificial Life, Autonomous Systems, Data Mining and Knowledge Discovery, Hybrid Intelligent Systems & Methods, Intelligent Agents, Multi-agent Systems, Intelligent Distributed Systems, Intelligent Information Retrieval, Intelligent/Natural Interactivity, Intelligent Virtual Environments, Knowledge Representation and Reasoning, Logic Programming, Knowledge-Based Systems, Machine Learning, Neural Nets, Genetic Algorithms, Natural Language Processing, Planning and Scheduling, Problem Solving, Constraint Satisfaction, Robotics, Machine Vision, Machine Sensing.

The Elements of Statistical Learning

Author : Trevor Hastie
Publisher : Springer Science & Business Media
ISBN 13 : 0387216065
Total Pages : 545 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis The Elements of Statistical Learning by : Trevor Hastie

Download or read book The Elements of Statistical Learning written by Trevor Hastie and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Deep Learning With Python

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 266 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning With Python by : Jason Brownlee

Download or read book Deep Learning With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-05-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.

Mastering Machine Learning with scikit-learn

Author : Gavin Hackeling
Publisher : Packt Publishing Ltd
ISBN 13 : 1788298497
Total Pages : 249 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Mastering Machine Learning with scikit-learn by : Gavin Hackeling

Download or read book Mastering Machine Learning with scikit-learn written by Gavin Hackeling and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

Advances in Bioinformatics and Computational Biology

Author : Katia S. Guimarães
Publisher : Springer Science & Business Media
ISBN 13 : 3642032230
Total Pages : 179 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Advances in Bioinformatics and Computational Biology by : Katia S. Guimarães

Download or read book Advances in Bioinformatics and Computational Biology written by Katia S. Guimarães and published by Springer Science & Business Media. This book was released on 2009-07-30 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the papers selected for presentation at the 4th Brazilian Sym- sium on Bioinformatics, BSB 2009, which was held in Porto Alegre, Brazil, during August 29–31, 2009. The BSB symposium had its origins in the Brazilian Workshop on Bioinformatics (WOB). WOB had three editions, in 2002 (Gramado, RS), in 2003 (Macaé, RJ), and in 2004 (Brasília, DF). The change in the designation from wo- shop to symposium reflects the increase in the quality of the contributions and also in the interest of the scientific community for the meeting. The previous editions of BSB took place in São Leopoldo, RS, in 2005, in Angra dos Reis, RJ, in 2007, and in Santo André, SP, in 2008. As evidence of the internationalization of the event, BSB 2009 had 55 submissions from seven countries. Of the 55 papers submitted, 36 were full papers, with up to 12 pages each, and 19 were extended abstracts, with up to 4 pages each. The articles submitted were carefully reviewed and selected by an international Program Comm- tee, comprising three chairs and 45 members from around the world, with the help of 21 additional reviewers. The Program Committee Chairs are very thankful to the - thors of all submitted papers, and especially to the Program Committee members and the additional reviewers, who helped select the 12 full papers and the six extended abstracts that make up this book.

Multiple Classifier Systems

Author : Neamat El Gayar
Publisher : Springer Science & Business Media
ISBN 13 : 3642121268
Total Pages : 337 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Multiple Classifier Systems by : Neamat El Gayar

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.

Multiple Classifier Systems

Author : Zhi-Hua Zhou
Publisher : Springer
ISBN 13 : 3642380670
Total Pages : 409 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Multiple Classifier Systems by : Zhi-Hua Zhou

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.

Multi-Label Dimensionality Reduction

Author : Liang Sun
Publisher : CRC Press
ISBN 13 : 1439806160
Total Pages : 206 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Multi-Label Dimensionality Reduction by : Liang Sun

Download or read book Multi-Label Dimensionality Reduction written by Liang Sun and published by CRC Press. This book was released on 2016-04-19 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks

Intelligent Computing and Information and Communication

Author : Subhash Bhalla
Publisher : Springer
ISBN 13 : 9811072450
Total Pages : 732 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Intelligent Computing and Information and Communication by : Subhash Bhalla

Download or read book Intelligent Computing and Information and Communication written by Subhash Bhalla and published by Springer. This book was released on 2018-01-19 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume presents high quality research papers presented at Second International Conference on Information and Communication Technology for Intelligent Systems (ICICC 2017). The conference was held during 2–4 August 2017, Pune, India and organized communally by Dr. Vishwanath Karad MIT World Peace University, Pune, India at MIT College of Engineering, Pune and supported by All India Council for Technical Education (AICTE) and Council of Scientific and Industrial Research (CSIR). The volume contains research papers focused on ICT for intelligent computation, communications and audio, and video data processing.

Designing Machine Learning Systems

Author : Chip Huyen
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098107918
Total Pages : 387 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Designing Machine Learning Systems by : Chip Huyen

Download or read book Designing Machine Learning Systems written by Chip Huyen and published by "O'Reilly Media, Inc.". This book was released on 2022-05-17 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems

Computational Vision and Bio-Inspired Computing

Author : S. Smys
Publisher : Springer Nature
ISBN 13 : 9811695733
Total Pages : 877 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Computational Vision and Bio-Inspired Computing by : S. Smys

Download or read book Computational Vision and Bio-Inspired Computing written by S. Smys and published by Springer Nature. This book was released on 2022-03-30 with total page 877 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes selected papers from the 5th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2021), held in Coimbatore, India, during November 25–26, 2021. This book presents state-of-the-art research innovations in computational vision and bio-inspired techniques. The book reveals the theoretical and practical aspects of bio-inspired computing techniques, like machine learning, sensor-based models, evolutionary optimization and big data modeling and management that make use of effectual computing processes in the bio-inspired systems. It also contributes to the novel research that focuses on developing bio-inspired computing solutions for various domains, such as human–computer interaction, image processing, sensor-based single processing, recommender systems and facial recognition, which play an indispensable part in smart agriculture, smart city, biomedical and business intelligence applications.

Artificial Neural Networks and Machine Learning – ICANN 2020

Author : Igor Farkaš
Publisher : Springer Nature
ISBN 13 : 3030616169
Total Pages : 891 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2020 by : Igor Farkaš

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2020 written by Igor Farkaš and published by Springer Nature. This book was released on 2020-10-17 with total page 891 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.