Download or read book Applications of Supervised and Unsupervised Ensemble Methods written by Oleg Okun and published by Springer. This book was released on 2009-10-15 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Applications of Supervised and Unsupervised Ensemble Methods written by Oleg Okun and published by Springer Science & Business Media. This book was released on 2009-10-06 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Supervised and Unsupervised Ensemble Methods and their Applications written by Oleg Okun and published by Springer. This book was released on 2008-04-20 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book results from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) in June 2007 in Girona, Spain. This workshop was held alongside the 3rd Iberian Conference on Pattern Recognition and Image Analysis.
Download or read book Ensemble Machine Learning written by Cha Zhang and published by Springer Science & Business Media. This book was released on 2012-02-17 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Ensemble Methods written by Zhi-Hua Zhou and published by CRC Press. This book was released on 2012-06-06 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
Download or read book Supervised and Unsupervised Learning for Data Science written by Michael W. Berry and published by Springer Nature. This book was released on 2019-09-04 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
Download or read book Ensembles in Machine Learning Applications written by Oleg Okun and published by Springer Science & Business Media. This book was released on 2011-09-07 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications.
Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Download or read book Machine Learning Foundations written by Taeho Jo and published by Springer Nature. This book was released on 2021-02-12 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Download or read book Recent Advances in Ensembles for Feature Selection written by Verónica Bolón-Canedo and published by Springer. This book was released on 2018-04-30 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.
Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Design of Interpretable Fuzzy Systems written by Krzysztof Cpałka and published by Springer. This book was released on 2017-01-31 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
Download or read book 7th International Conference on Knowledge Management in Organizations Service and Cloud Computing written by Lorna Uden and published by Springer Science & Business Media. This book was released on 2012-07-18 with total page 593 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seventh International Conference on Knowledge Management in Organizations (KMO) brings together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on knowledge management in organisations. KMO 2012 provides an international forum for authors to present and discuss research focused on the role of knowledge management for innovative services in industries, to shed light on recent advances in cloud computing for KM as well as to identify future directions for researching the role of knowledge management in service innovation and how cloud computing can be used to address many of the issues currently facing KM in academia and industrial sectors. The conference took place at Salamanca in Spain on the 11th-13th July in 2012.
Download or read book Machine Learning and Knowledge Discovery in Databases written by Peter A. Flach and published by Springer. This book was released on 2012-09-08 with total page 904 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.
Download or read book Multiple Classifier Systems written by Carlo Sansone and published by Springer. This book was released on 2011-09-15 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 Hands On Unsupervised Learning Using Python written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2019-02-21 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks
