Advances in Robot Learning, Jeremy Wyatt; John Demiris
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 124150.00 T Наличие на складе: Невозможна поставка. Описание:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Название: Recent advances in reinforcement learning ISBN: 0792397053 ISBN-13(EAN): 9780792397052 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Addresses research in the Artificial Intelligence and Neural Network communities. This book includes topics such as the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques.
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: 241310.00 T 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.
Автор: Ella Bingham Название: Advances in Independent Component Analysis and Learning Machines ISBN: 0128028068 ISBN-13(EAN): 9780128028063 Издательство: Elsevier Science Рейтинг: Цена: 128010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.
Examples of topics which have developed from the advances of ICA, which are covered in the book are:
A unifying probabilistic model for PCA and ICA
Optimization methods for matrix decompositions
Insights into the FastICA algorithm
Unsupervised deep learning
Machine vision and image retrieval
A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning
A diverse set of application fields, ranging from machine vision to science policy data
Contributions from leading researchers in the field
Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils Название: Advances in Learning Classifier Systems ISBN: 3540424377 ISBN-13(EAN): 9783540424376 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: These are the refereed post-proceedings of the Third International Workshop on Learning Classifier Systems, IWLCS 2000. The papers are organized in topical sections on theory, applications, and advanced architectures.
Автор: Judy A. Franklin; Tom M. Mitchell; Sebastian Thrun Название: Recent Advances in Robot Learning ISBN: 0792397452 ISBN-13(EAN): 9780792397458 Издательство: Springer Рейтинг: Цена: 158340.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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