cost sensitive machine learning chapman hall crc machine learning pattern recognition

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Cost Sensitive Machine Learning

Author : Balaji Krishnapuram
ISBN : 9781439839287
Genre : Computers
File Size : 41. 43 MB
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In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation, and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

A First Course In Machine Learning Second Edition

Author : Simon Rogers
ISBN : 9781498738545
Genre : Business & Economics
File Size : 45. 77 MB
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"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." —Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." —Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength...Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." —David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning...The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." —Guangzhi Qu, Oakland University, Rochester, Michigan, USA

Machine Learning

Author : Stephen Marsland
ISBN : 9781498759786
Genre : Computers
File Size : 23. 77 MB
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A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

Computational Trust Models And Machine Learning

Author : Xin Liu
ISBN : 9781482226669
Genre : Computers
File Size : 69. 95 MB
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Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

Ensemble Methods

Author : Zhi-Hua Zhou
ISBN : 9781439830031
Genre : Business & Economics
File Size : 68. 87 MB
Format : PDF
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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.

Component And Systems Diagnostics Prognosis And Health Management

Author : Peter Kingsford Willett
ISBN : 0819444839
Genre : Automatic test equipment
File Size : 54. 91 MB
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Fuzzy Clusteranalyse

Author : Frank Höppner
ISBN : 9783322868367
Genre : Technology & Engineering
File Size : 82. 25 MB
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Dieses Buch ist das Standardwerk zu einem neuen Bereich der angewandten Fuzzy-Technologie, der Fuzzy-Clusteranalyse. Diese beinhaltet Verfahren der Mustererkennung zur Gruppierung und Strukturierung von Daten. Dabei werden im Gegensatz zu klassischen Clustering-Techniken die Daten nicht eindeutig zu Klassen zugeordnet, sondern Zugehörigkeitsgrade bestimmt, so daß die Fuzzy-Verfahren robust gegenüber gestörten oder verrauschten Daten sind und fließende Klassenübergänge handhaben können. Dieses Werk gibt eine methodische Einführung in die zahlreichen Fuzzy-Clustering-Algorithmen mit ihren Anwendungen in den Bereichen Datenanalyse, Erzeugung von Regeln für Fuzzy-Regler, Klassifikations- und Approximationsprobleme sowie eine ausführliche Darstellung des Shell-Clustering zur Erkennung von geometrischen Konturen in Bildern.

Data Mining

Author : Ian H. Witten
ISBN : 3446215336
Genre :
File Size : 50. 76 MB
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Vorlesungen Ber Funktionalgleichungen Und Ihre Anwendungen

Author : J. Aczel
ISBN : 9783034869041
Genre : Juvenile Nonfiction
File Size : 52. 11 MB
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Real Time Data Mining

Author : Florian Stompe
ISBN : 9783836678797
Genre : Business & Economics
File Size : 89. 50 MB
Format : PDF
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Data Mining ist ein inzwischen etabliertes, erfolgreiches Werkzeug zur Extraktion von neuem, bislang unbekanntem Wissen aus Daten. In mittlerweile fast allen gr eren Unternehmen wird es genutzt um Mehrwerte f r Kunden zu generieren, den Erfolg von Marketingkampagnen zu erh hen, Betrugsverdacht aufzudecken oder beispielsweise durch Segmentierung unterschiedliche Kundengruppen zu identifizieren. Ein Grundproblem der intelligenten Datenanalyse besteht darin, dass Daten oftmals in rasanter Geschwindigkeit neu entstehen. Eink ufe im Supermarkt, Telefonverbindungen oder der ffentliche Verkehr erzeugen t glich eine neue Flut an Daten, in denen potentiell wertvolles Wissen steckt. Die versteckten Zusammenh nge und Muster k nnen sich im Zeitverlauf mehr oder weniger stark ver ndern. Datenmodellierung findet in der Regel aber noch immer einmalig bzw. sporadisch auf dem Snapshot einer Datenbank statt. Einmal erkannte Muster oder Zusammenh nge werden auch dann noch angenommen, wenn diese l ngst nicht mehr bestehen. Gerade in dynamischen Umgebungen wie zum Beispiel einem Internet-Shop sind Data Mining Modelle daher schnell veraltet. Betrugsversuche k nnen dann unter Umst nden nicht mehr erkannt, Absatzpotentiale nicht mehr genutzt werden oder Produktempfehlungen basieren auf veralteten Warenk rben. Um dauerhaft Wettbewerbsvorteile erzielen zu k nnen, muss das Wissen ber Daten aber m glichst aktuell und von ausgezeichneter Qualit t sein. Der Inhalt dieses Buches skizziert Methoden und Vorgehensweisen von Data Mining in Echtzeit.

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