Way M.J., Scargle J.D., Ali K.M., Srivastava A.N. (eds.) Advances in Machine Learning and Data Mining for Astronomy

Chapman&Hall/CRC Press, 2012. — 694.Part I Foundational Issues
Classification in Astronomy: Past and Present
Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy
Probability and Statistics in Astronomical Machine Learning and Data Mining
Part II Astronomical Applications
Section 1 Source Identification
Automated Science Processing for the Fermi Large Area Telescope
Cosmic Microwave Background Data Analysis
Data Mining and Machine Learning in Time-Domain Discovery and Classification
Cross-Identification of Sources: Theory and Practice
The Sky Pixelization for Cosmic Microwave Background Mapping
Future Sky Surveys: New Discovery Frontiers
Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi Data
Section 2 Classification
Galaxy Zoo: Morphological Classification and Citizen Science
The Utilization of Classifications in High-Energy Astrophysics Experiments
Database-Driven Analyses of Astronomical Spectra
Weak Gravitational Lensing
Photometric Redshifts: 50 Years After
Galaxy Clusters
Section 3 Signal Processing (Time-Series) Analysis
Planet Detection: The Kepler Mission
Classification of Variable Objects in Massive Sky Monitoring Surveys
Gravitational Wave Astronomy
Section 4 The Largest Data Sets
Virtual Observatory and Distributed Data Mining
Multitree Algorithms for Large-Scale Astrostatistics
Part III Machine Learning Methods
Time–Frequency Learning Machines for Nonstationarity Detection Using Surrogates Classification
On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models
Data Clustering
Ensemble Methods: A Review
Parallel and Distributed Data Mining for Astronomy Applications
Pattern Recognition in Time Series
Randomized Algorithms for Matrices and Data