Fractal Approaches for Modeling Financial Assets and Predicting Crises, Inna Nekrasova, Oxana Karnaukhova, Bryan Christiansen

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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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.

Автор: Robert M. Losee
Название: Predicting Information Retrieval Performance
ISBN: 1681734745 ISBN-13(EAN): 9781681734743
Издательство: Mare Nostrum (Eurospan)
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Цена: 61910.00 T
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Описание: Within the constraints of alien control or influence, it is argued, cultural and organisational barriers have consistently allowed a wide range of initiative to African leaders and communities in a creative and flexible adjustment to new and unfamiliar demands. Exploration of this African initiative in various contexts suggests a complex, fascinating pattern of cultural and structural interaction.

Автор: F.C.G.A. Nicolleau; C. Cambon; J.-M. Redondo; J.C.
Название: New Approaches in Modeling Multiphase Flows and Dispersion in Turbulence, Fractal Methods and Synthetic Turbulence
ISBN: 9400736940 ISBN-13(EAN): 9789400736948
Издательство: Springer
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Цена: 104480.00 T
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Описание: This key volume provides the best syntheses on the current status of research in kinematic simulation and other synthetic turbulence models applied to environmental flows. KS itself is widely used in various domains including Lagrangian dispersion.

Автор: Wang
Название: Evolution, Monitoring and Predicting Models of Rockburst
ISBN: 9811075476 ISBN-13(EAN): 9789811075476
Издательство: Springer
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Цена: 46570.00 T
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Introduction.- Experimental Materials and Equipment.- The Mechanism and Predicting Theory-Based Rockburst Evolution.- Three-dimensional Reconstruction Model and Numerical Simulation of Rock Fissures.- The Patterns of Dynamic Evolution of Cracks in Rock Failure.- Experiment Investigation of AE Precursor Information for Rockburst.- Experimental Investigations on Multi-means and Synergistic Prediction for Rockburst.- Predicting Model of Rockburst Based on Nondeterministic Theory.- Field Case.

Автор: Brown, James L
Название: Predicting staying in or leaving permanent supportive housing that serves homeless people with serious mental illness - scholar`s choice edition
ISBN: 1297051505 ISBN-13(EAN): 9781297051500
Издательство: Неизвестно
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Цена: 24210.00 T
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