Michael Way

Book: Advances in Machine Learning and Data Mining for Astronomy

Editors: Michael Way, Jeff Scargle, Kamal Ali, Ashok Srivastava

Publisher: Chapman and Hall an imprint of CRC Press (a division of Taylor and Francis)
Randi Cohen, Computer Science Acquisitions Editor
Part of the Data Mining and Knowledge Discovery series

Introduction: Astronomy and Models in early history, summary of book contents, Author: The Editors

Part I Foundational Issues/Introduction

1. Classification in Astronomy: Past and Present, Author: Eric Feigelson

2. Searching the Heavens: Astronomy, Computation, Statistics, Data Mining and Philosophy, Author: Clark Glymour

3. Probability and Statistics in Astronomical Machine Learning and Data Mining, Author: Jeff Scargle

Part II Astronomical Applications

Section 1: Source Identification

4. Automated Science facilty for Fermi, Author: Jim Chiang

5. Cosmic Microwave Background Studies with Machine Learning, Authors: J.L. Starck and Paniez Paykari

6. Data Mining and Machine-Learning in Time-Domain Discovery and Classification, Author: Josh Bloom and Joseph Richards

7. Cross-Identification of Sources: Theory and Practice , Author: Tamas Budavari

8. The sky pixelization for CMB mapping, Authors: Oleg Verkhodanov and Andre Doroshkevich

9. Future Sky Surveys: New Discovery Frontiers, Authors: Tony Tyson and Kirk Borne

10. Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi data, Authors: Schmitt, Starck, Fadili, and Digel

Section 2: Classification

11. Galaxy Zoo: Morphological Classification and Citizen Science, Authors: Fortson, Masters, Nichol, Borne, Edmondson, Lintott, Raddick, Schawinski, Wallin

12. The Utilization of Classifications in High Energy Astrophysics Experiments, Author: Bill Atwood

13. Database-driven analyses of astronomical spectra , Author: Jan Cami

14. Weak Gravitational Lensing, Authors: J.L. Starck and Sandrine Pires

15. Photometric Redshifts: 50 years after, Author: Tamas Budavari

16. Galaxy Clusters , Author: Chris Miller

Section 3: Signal Processing (Time Series) Analysis

17. Planet Detection: The Kepler Mission, Author: Jon Jenkins

18. Classification of Variable Objects in Massive Sky Monitoring Surveys, Authors: Przemek Wozniak, Lukasz Wyrzykowski, Vasily Belokurov

19. Gravitational Wave Astronomy, Author: Sam Finn

Section 4: The Largest Data Sets

20. Virtual Observatory and Distributed Data Mining, Author: Kirk Borne

21. Multi-Tree Algorithms for Large-Scale Astrostatistics , Authors: William B. March, Arkadas Ozakin, Dongryeol Lee, Ryan Riegel, and Alexander G. Gray

Part III Machine Learning Methods

22. Time-Frequency Learning Machines For NonStationarity Detection Using Surrogates, Authors: Pierre Borgnat, Patrick Flandrin, Cedric Richard, Andre Ferrari, Hassan Amoud, and Paul Honeine

23. Classification , Author: Nikunj Oza

24. On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models , Author: Bill Heavlin

25. Data Clustering , Author: Kiri Wagstaff

26. Ensemble Methods: A Review , Author: Giorgio Valentini and Matteo Re

27. Parallel and distributed data mining for astronomy applications , Authors: Kamalika Das and Kanishka Bhaduri

28. Pattern Recognition in Time Series , Authors: Jessica Lin, Sheri Williamson, Kirk Borne and David DeBarr

29. Randomized algorithms for matrices and data, Author: Mike Mahoney