Pattern Classification Using Ensemble Methods

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Pattern Classification Using Ensemble Methods

Author : Lior Rokach
ISBN : 9789814271073
Genre : Computers
File Size : 36.19 MB
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Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method. Sample Chapter(s). Chapter 1: Introduction to Pattern Classification (246 KB). Contents: Introduction to Pattern Classification; Introduction to Ensemble Learning; Ensemble Classification; Ensemble Diversity; Ensemble Selection; Error Correcting Output Codes; Evaluating Ensembles of Classifiers. Readership: Researchers, advanced undergraduate and graduate students in machine learning and pattern recognition.
Category: Computers

Ensemble Learning Pattern Classification Using Ensemble Methods Second Edition

Author : Lior Rokach
ISBN : 9789811201974
Genre : Computers
File Size : 36.61 MB
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This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
Category: Computers

Ensemble Learning

Author : Lior Rokach
ISBN : 981120196X
Genre : COMPUTERS
File Size : 86.72 MB
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Category: COMPUTERS

Multiple Classifier Systems

Author : Zhi-Hua Zhou
ISBN : 9783642380679
Genre : Computers
File Size : 89.84 MB
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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.
Category: Computers

Pattern Recognition

Author : Axel Pinz
ISBN : 9783642327179
Genre : Computers
File Size : 71.6 MB
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This book constitutes the refereed proceedings of the 34th Symposium of the German Association for Pattern Recognition, DAGM 2012, and the 36th Symposium of the Austrian Association for Pattern Recognition, OAGM 2012, held in Graz, Austria, in August 2012. The 27 revised full papers and 23 revised poster papers were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on segmentation, low-level vision, 3D reconstruction, recognition, applications, learning, and features.
Category: Computers

Ensembles For Supervised Classification Learning

Author : Ofer Matan
ISBN : STANFORD:36105019798649
Genre : Algorithms
File Size : 28.92 MB
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This dissertation studies the use of multiple classifiers (ensembles or committees) in learning tasks. Both theoretical and practical aspects of combining classifiers are studied.
Category: Algorithms

Author : 日本オペレーションズ・リサーチ学会
ISBN : UOM:39015072640256
Genre : Operations research
File Size : 52.65 MB
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Category: Operations research

Combining Pattern Classifiers

Author : Ludmila I. Kuncheva
ISBN : 0471660256
Genre : Technology & Engineering
File Size : 37.72 MB
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Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.
Category: Technology & Engineering

Ensembles In Machine Learning Applications

Author : Oleg Okun
ISBN : 9783642229107
Genre : Computers
File Size : 41.66 MB
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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.
Category: Computers

Progress In Pattern Recognition Image Analysis And Applications

Author : José Ruiz-Shulcloper
ISBN : 9783540859192
Genre : Computers
File Size : 27.91 MB
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The 13th Iberoamerican Congress on Pattern Recognition (CIARP 2008, C- greso IberoAmericano de Reconocimiento de Patrones) was the 13th event in a seriesofpioneercongressesonpatternrecognitioninthecommunityofIberoam- icancountries. As was the case for previous conferences, CIARP 2008 attracted participants from around the world with the aim of promoting and disseminating ongoing researchon mathematical methods and computing techniques for pattern rec- nition, computer vision, image analysis, and speech recognition, as well as their application in such diverse areas as robotics, industry, health, entertainment, space exploration, telecommunications, data mining, document analysis, and natural language processing and recognition to name only few of them. Mo- over, it provided a forum for scienti?c research, experience exchange, share of new knowledge and an increase in cooperation between research groups in p- tern recognition and related areas. It is important to underline the fact that these conferences have contributed signi?cantly to the growth of national associations for pattern recognitionin the Iberoamerican region, all of them as members of the International Association for Pattern Recognition (IAPR). This year, the forum attracted participants from 34 countries (8 from Iberoamerica), who submitted a total of 182 papers. After a serious reviewing process in which allthe submissions where double-blind reviewedby at least two prestigiousreviewers,93 papersby180authors from29 countries wereaccepted, all of them with a scienti?c quality above overall mean rating.
Category: Computers

Intelligent Computing In Bioinformatics

Author : De-Shuang Huang
ISBN : 9783319093307
Genre : Computers
File Size : 42.52 MB
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This book – in conjunction with the volumes LNCS 8588 and LNAI 8589 – constitutes the refereed proceedings of the 10th International Conference on Intelligent Computing, ICIC 2014, held in Taiyuan, China, in August 2014. The 58 papers of this volume were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections such as machine learning; neural networks; image processing; computational systems biology and medical informatics; biomedical informatics theory and methods; advances on bio-inspired computing; protein and gene bioinformatics: analysis, algorithms, applications.
Category: Computers

Hands On Ensemble Learning With Python

Author : George Kyriakides
ISBN : 9781789617887
Genre : Computers
File Size : 53.80 MB
Format : PDF
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Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and Keras Book Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learn Implement ensemble methods to generate models with high accuracy Overcome challenges such as bias and variance Explore machine learning algorithms to evaluate model performance Understand how to construct, evaluate, and apply ensemble models Analyze tweets in real time using Twitter's streaming API Use Keras to build an ensemble of neural networks for the MovieLens dataset Who this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.
Category: Computers

Learning Data Mining With R

Author : Bater Makhabel
ISBN : 9781783982110
Genre : Computers
File Size : 33.76 MB
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This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.
Category: Computers

Ensemble Methods

Author : Zhi-Hua Zhou
ISBN : 9781439830031
Genre : Business & Economics
File Size : 89.15 MB
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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.
Category: Business & Economics

Ijcai 05

Author : Leslie Pack Kaelbling
ISBN : UCSD:31822030088785
Genre : Artificial intelligence
File Size : 62.9 MB
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Category: Artificial intelligence

Mastering Machine Learning With Spark 2 X

Author : Alex Tellez
ISBN : 9781785282416
Genre : Computers
File Size : 46.5 MB
Format : PDF
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Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment. Style and approach This book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of machine learning, through Spark, in a variety of enterprise-grade systems.
Category: Computers