New Developments in Unsupervised Outlier Detection: Algorithms and Applications, Wang Xiaochun, Wang Xiali, Wilkes Mitch
Автор: Marius Leordeanu Название: Unsupervised Learning in Space and Time ISBN: 3030421279 ISBN-13(EAN): 9783030421274 Издательство: Springer Рейтинг: Цена: 18294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video.
The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Описание: Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Ensembles of Classification Methods and Their Applications.- Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.- Erratum.
Описание: Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Ensembles of Classification Methods and Their Applications.- Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.- Erratum.
Описание: This book contains the extended papers presented at the 2nd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA)heldon21-22July,2008inPatras, Greece, inconjunctionwiththe 18thEuropeanConferenceon Arti?cial Intelligence(ECAI 2008). This wo- shop was a successor of the smaller event held in 2007 in conjunction with 3rd Iberian Conference on Pattern Recognition and Image Analysis, Girona, Spain. The success of that event as well as the publication of workshop - pers in the edited book "Supervised and Unsupervised Ensemble Methods and their Applications", published by Springer-Verlag in Studies in Com- tational Intelligence Series in volume 126, encouraged us to continue a good tradition. The scope of both SUEMA workshops (hence, the book as well) is the application of theoretical ideas in the ?eld of ensembles of classi?cation and clusteringalgorithmstoreal/lifeproblemsinscienceandindustry. Ensembles, which represent a number of algorithms whose class or cluster membership predictions are combined together to produce a single outcome value, have alreadyprovedto be a viable alternativeto a single best algorithmin various practical tasks under di?erent scenarios, from bioinformatics to biometrics, from medicine to network security. The ensemble approach is caused to life by the famous "no free lunch" theorem, stating that there is no absolutely best algorithm to solve all problems. Although ensembles cannot be cons- ered as absolute remedy of a single algorithm de?ciency, it is widely believed thatensemblesprovideabetteranswerto"nofreelunch"theoremthanas- glebestalgorithm. Statistical, algorithmical, representational, computational and practical reasons can explain the success of ensemble methods.
Описание: 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.
Описание: 2 Books in 1 Do not miss out on the bundle book offer 300+ pages of valuable content What You'll Learn Book 1 Machine Learning You will learn the fundamentals of machine learning from algorithms, python, supervised and unsupervised learning Concepts such as " decision trees " & "random forest introduction" are explained in detail and come with visual diagrams to assist in grasping the subject matter You will learn real world applications of machine learning, artificial intelligence and understand how it will effect humanity in the upcoming years The world is constantly changing and evolving, transportation was revolutionized by cars and planes, however, "machine learning" will revolutionize the world in which we live from simple day to day tasks to even the most complex endeavors Book 2 Markov Models In the segment of the bundle book you will learn the mathematics behind Markov Models algorithms, artificial intelligence, weather reporting, Bayesian inference, tools, solutions and much, much more You will gain insights to the 3 main problems of Markov Models and learn how to overcome them. You will also learn about the real world applications, implications and theories of Markov Models This is an incredible offer you do not want to miss out on This bundle book offer gives you so much value at an affordable price you won't find anywhere else What are you waiting for? Grab your copy now Note* For the best visual experience of diagrams it is highly recommended you purchases the paperback version of the bundle book offer First time audible listeners get a 30 day free-trial and 2 free audible books when signing up for the first time. Audible Link: https: //www.audible.com/t2/title?asin=B078C8J4SG
Автор: Celebi M. Emre, Aydin Kemal Название: Unsupervised Learning Algorithms ISBN: 3319795902 ISBN-13(EAN): 9783319795904 Издательство: Springer Рейтинг: Цена: 13415.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners.
Автор: M. Emre Celebi; Kemal Aydin Название: Unsupervised Learning Algorithms ISBN: 3319242091 ISBN-13(EAN): 9783319242095 Издательство: Springer Рейтинг: Цена: 12196.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners.
Описание: With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You`ll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.
Автор: Oliver Kramer Название: Dimensionality Reduction with Unsupervised Nearest Neighbors ISBN: 3662518953 ISBN-13(EAN): 9783662518953 Издательство: Springer Рейтинг: Цена: 14817.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems
Key Features
Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
Master the art of data-driven problem-solving with hands-on examples
Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms
Book Description
Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
What you will learn
Understand when to use supervised, unsupervised, or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
Apply supervised and unsupervised algorithms to overcome various machine learning challenges
Employ best practices for tuning your algorithm's hyper parameters
Discover how to use neural networks for classification and regression
Build, evaluate, and deploy your machine learning solutions to production
Who this book is for
This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
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