Predicting the Dynamics of Research Impact, Manolopoulos Yannis, Vergoulis Thanasis

Автор: Diane J. Cook,Narayanan C. Krishnan
Название: Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data
ISBN: 111889376X ISBN-13(EAN): 9781118893760
Издательство: Wiley
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Цена: 104490.00 T
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Описание: Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data.

Автор: Atin Basuchoudhary; James T. Bang; Tinni Sen
Название: Machine-learning Techniques in Economics
ISBN: 3319690132 ISBN-13(EAN): 9783319690131
Издательство: Springer
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Цена: 51230.00 T
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Описание: This book develops a machine-learning framework for predicting economic growth. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.

Автор: Yogesh K. Dwivedi; Michael R. Wade; Scott L. Schne
Название: Information Systems Theory
ISBN: 1461429706 ISBN-13(EAN): 9781461429708
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book details the many prominent theories, models and related research approaches of Information Systems (IS) research to provide a comprehensive understanding of the subject, making it a practical reference for practitioners and researchers.

Название: Predicting structured data
ISBN: 0262528045 ISBN-13(EAN): 9780262528047
Издательство: MIT Press
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Цена: 57030.00 T
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Описание:

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field.

Contributors
Yasemin Altun, Gokhan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daume III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Perez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Scholkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston

Автор: Robert M. Losee
Название: Predicting Information Retrieval Performance
ISBN: 1681734729 ISBN-13(EAN): 9781681734729
Издательство: Mare Nostrum (Eurospan)
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Цена: 41580.00 T
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Описание: Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively.This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.