Machine Learning and Knowledge Discovery in Databases, Walter Daelemans; Katharina Morik
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Автор: Mitchell Название: Machine Learning ISBN: 0071154671 ISBN-13(EAN): 9780071154673 Издательство: McGraw-Hill Рейтинг: Цена: 10466.00 р. Наличие на складе: Поставка под заказ.
Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
Автор: Clarke Название: Principles and Theory for Data Mining and Machine Learning ISBN: 0387981349 ISBN-13(EAN): 9780387981345 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering
Автор: Jacek Koronacki; Zbigniew W. Ras; Slawomir T. Wier Название: Advances in Machine Learning II ISBN: 3642051782 ISBN-13(EAN): 9783642051784 Издательство: Springer Рейтинг: Цена: 36197.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: General Issues.- Knowledge-Oriented and Distributed Unsupervised Learning for Concept Elicitation.- Toward Interactive Computations: A Rough-Granular Approach.- Data Privacy: From Technology to Economics.- Adapting to Human Gamers Using Coevolution.- Wisdom of Crowds in the Prisoner's Dilemma Context.- Logical and Relational Learning, and Beyond.- Towards Multistrategic Statistical Relational Learning.- About Knowledge and Inference in Logical and Relational Learning.- Two Examples of Computational Creativity: ILP Multiple Predicate Synthesis and the 'Assets' in Theorem Proving.- Logical Aspects of the Measures of Interestingness of Association Rules.- Text and Web Mining.- Clustering the Web 2.0.- Induction in Multi-Label Text Classification Domains.- Cluster-Lift Method for Mapping Research Activities over a Concept Tree.- On Concise Representations of Frequent Patterns Admitting Negation.- Classification and Beyond.- A System to Detect Inconsistencies between a Domain Expert's Different Perspectives on (Classification) Tasks.- The Dynamics of Multiagent Q-Learning in Commodity Market Resource Allocation.- Simple Algorithms for Frequent Item Set Mining.- Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification.- Machine Learning Methods in Automatic Image Annotation.- Neural Networks and Other Nature Inspired Approaches.- Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework.- Machine Learning in Vector Models of Neural Networks.- Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction.- Discovering Data Structures Using Meta-learning, Visualization and Constructive Neural Networks.- Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection.- Immunocomputing for Speaker Recognition.
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