Advances in Machine Learning and Data Mining for Astronomy

  • Producent: Taylor
  • Oprawa: Twarda
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Opis: Advances in Machine Learning and Data Mining for Astronomy - Michael J. Way

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.Part I: Foundational Issues Classification in Astronomy: Past and Present, Eric Feigelson Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy, Clark Glymour Probability and Statistics in Astronomical Machine Learning and Data Mining, Jeffrey D. Scargle Part II: Astronomical Applications Source Identification Automated Science Processing for the Fermi Large Area Telescope, James Chiang CMB Data Analysis, Paniez Paykari and Jean-Luc Starck Data Mining and Machine Learning in Time-Domain Discovery and Classification, Joshua S. Bloom and Joseph W. Richards Cross-Identification of Sources: Theory and Practice, Tamas Budavari The Sky Pixelization for CMB Mapping, O.V. Verkhodanov and A.G. Doroshkevich Future Sky Surveys: New Discovery Frontiers, J. Anthony Tyson and Kirk D. Borne Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi Data, Jeremy Schmitt, Jean-Luc Starck, Jalal Fadili, and Seth Digel Classification Galaxy Zoo: Morphological Classification and Citizen Science, Lucy Fortson, Karen Masters, Robert Nichol, Kirk D. Borne, Edd Edmondson, Chris Lintoot, Jordan Raddick, Kevin Schawinski, and John Wallin The Utilization of Classifications in High-Energy Astrophysics Experiments, Bill Atwood Database-Driven Analyses of Astronomical Spectra, Jan Cami Weak Gravitational Lensing, Sandrine Pires, Jean-Luc Starck, Adrienne Leonard, and Alexandre Refregier Photometric Redshifts: 50 Years after 345, Tamas Budavari Galaxy Clusters, Christopher J. Miller Signal Processing (Time-Series) Analysis Planet Detection: The Kepler Mission, Jon M. Jenkins, Jeffrey C. Smith, Peter Tenenbaum, Joseph D. Twicken, and Jeffrey Van Cleve Classification of Variable Objects in Massive Sky Monitoring Surveys, Przemek Wozniak, Lukasz Wyrzykowski, and Vasily Belokurov Gravitational Wave Astronomy, Lee Samuel Finn The Largest Data Sets Virtual Observatory and Distributed Data Mining, Kirk D. Borne Multitree Algorithms for Large-Scale Astrostatistics, William B. March, Arkadas Ozakin, Dongryeol Lee, Ryan Riegel, and Alexander G. Gray PART III: Machine Learning Methods Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates, Pierre Borgnat, Patrick Flandrin, Cedric Richard, Andre Ferrari, Hassan Amoud, and Paul Honeine Classification, Nikunj Oza On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models, William D. Heavlin Data Clustering, Kiri L. Wagstaff Ensemble Methods: A Review, Matteo Re and Giorgio Valentini Parallel and Distributed Data Mining for Astronomy Applications, Kamalika Das and Kanishka Bhaduri Pattern Recognition in Time Series, Jessica Lin, Sheri Williamson, Kirk D. Borne, and David De Barr Randomized Algorithms for Matrices and Data, Michael W. Mahoney Index


Szczegóły: Advances in Machine Learning and Data Mining for Astronomy - Michael J. Way

Tytuł: Advances in Machine Learning and Data Mining for Astronomy
Autor: Michael J. Way
Producent: Taylor
ISBN: 9781439841730
Rok produkcji: 2012
Ilość stron: 744
Oprawa: Twarda
Waga: 1.47 kg


Recenzje: Advances in Machine Learning and Data Mining for Astronomy - Michael J. Way

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Przypomnij hasło
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Advances in Machine Learning and Data Mining for Astronomy

  • Producent: Taylor
  • Oprawa: Twarda

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.Part I: Foundational Issues Classification in Astronomy: Past and Present, Eric Feigelson Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy, Clark Glymour Probability and Statistics in Astronomical Machine Learning and Data Mining, Jeffrey D. Scargle Part II: Astronomical Applications Source Identification Automated Science Processing for the Fermi Large Area Telescope, James Chiang CMB Data Analysis, Paniez Paykari and Jean-Luc Starck Data Mining and Machine Learning in Time-Domain Discovery and Classification, Joshua S. Bloom and Joseph W. Richards Cross-Identification of Sources: Theory and Practice, Tamas Budavari The Sky Pixelization for CMB Mapping, O.V. Verkhodanov and A.G. Doroshkevich Future Sky Surveys: New Discovery Frontiers, J. Anthony Tyson and Kirk D. Borne Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi Data, Jeremy Schmitt, Jean-Luc Starck, Jalal Fadili, and Seth Digel Classification Galaxy Zoo: Morphological Classification and Citizen Science, Lucy Fortson, Karen Masters, Robert Nichol, Kirk D. Borne, Edd Edmondson, Chris Lintoot, Jordan Raddick, Kevin Schawinski, and John Wallin The Utilization of Classifications in High-Energy Astrophysics Experiments, Bill Atwood Database-Driven Analyses of Astronomical Spectra, Jan Cami Weak Gravitational Lensing, Sandrine Pires, Jean-Luc Starck, Adrienne Leonard, and Alexandre Refregier Photometric Redshifts: 50 Years after 345, Tamas Budavari Galaxy Clusters, Christopher J. Miller Signal Processing (Time-Series) Analysis Planet Detection: The Kepler Mission, Jon M. Jenkins, Jeffrey C. Smith, Peter Tenenbaum, Joseph D. Twicken, and Jeffrey Van Cleve Classification of Variable Objects in Massive Sky Monitoring Surveys, Przemek Wozniak, Lukasz Wyrzykowski, and Vasily Belokurov Gravitational Wave Astronomy, Lee Samuel Finn The Largest Data Sets Virtual Observatory and Distributed Data Mining, Kirk D. Borne Multitree Algorithms for Large-Scale Astrostatistics, William B. March, Arkadas Ozakin, Dongryeol Lee, Ryan Riegel, and Alexander G. Gray PART III: Machine Learning Methods Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates, Pierre Borgnat, Patrick Flandrin, Cedric Richard, Andre Ferrari, Hassan Amoud, and Paul Honeine Classification, Nikunj Oza On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models, William D. Heavlin Data Clustering, Kiri L. Wagstaff Ensemble Methods: A Review, Matteo Re and Giorgio Valentini Parallel and Distributed Data Mining for Astronomy Applications, Kamalika Das and Kanishka Bhaduri Pattern Recognition in Time Series, Jessica Lin, Sheri Williamson, Kirk D. Borne, and David De Barr Randomized Algorithms for Matrices and Data, Michael W. Mahoney Index

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Cena 345,50 PLN
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Szczegóły: Advances in Machine Learning and Data Mining for Astronomy - Michael J. Way

Tytuł: Advances in Machine Learning and Data Mining for Astronomy
Autor: Michael J. Way
Producent: Taylor
ISBN: 9781439841730
Rok produkcji: 2012
Ilość stron: 744
Oprawa: Twarda
Waga: 1.47 kg


Recenzje: Advances in Machine Learning and Data Mining for Astronomy - Michael J. Way

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