This book shows that astronomical discovery is a complex and ongoing process comprising various stages of research, interpretation and understanding.

Since the invention of the telescope 400 years ago, astronomers have rapidly discovered countless celestial objects. But how does one make sense of it all? Astronomer and former NASA Chief Historian Steven J. Dick brings order to this menagerie by defining 82 classes of astronomical objects, which he places in a beginner-friendly system known as "Astronomy’s Three Kingdoms.” Rather than concentrating on technicalities, this system focuses on the history of each object, the nature of its discovery, and our current knowledge about it. The ensuing book can therefore be read on at least two levels. On one level, it is an illustrated guide to various types of astronomical wonders. On another level, it is considerably more: the first comprehensive classification system to cover all celestial objects in a consistent manner. Accompanying each spread are spectacular historical and modern images. The result is a pedagogical tour-de-force, whereby readers can easily master astronomy’s three realms of planets, stars, and galaxies.

Astronomical surveys produce large amounts of photometric, spectroscopic and time-series data. Object classification, parameter determination, novelty detection and the discovery of structure in these are challenging tasks. This book, featuring contributions from both astronomers and computer scientists, discusses a broad range of astronomical problems and shows how various machine learining and statistical analysis techniques are being used to solve them.

A thought provoking study of the powerful impact of images in guiding astronomers' understanding of galaxies through time.

Astronomers and astrophysicists are making revolutionary advances in our understanding of planets, stars, galaxies, and even the structure of the universe itself. The Decade of Discovery presents a survey of this exciting field of science and offers a prioritized agenda for space- and ground-based research into the twenty-first century. The book presents specific recommendations, programs, and expenditure levels to meet the needs of the astronomy and astrophysics communities. Accessible to the interested lay reader, the book explores: The technological investments needed for instruments that will be built in the next century. The importance of the computer revolution to all aspects of astronomical research. The potential usefulness of the moon as an observatory site. Policy issues relevant to the funding of astronomy and the execution of astronomical projects. The Decade of Discovery will prove valuable to science policymakers, research administrators, scientists, and students in the physical sciences, and interested lay readers.

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

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

Annotation. This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2010, held in Barcelona, Spain, in September 2010. The 120 revised full papers presented in three volumes, together with 12 demos (out of 24 submitted demos), were carefully reviewed and selected from 658 paper submissions. In addition, 7 ML and 7 DM papers were distinguished by the program chairs on the basis of their exceptional scientific quality and high impact on the field. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. A topic widely explored from both ML and DM perspectives was graphs, with motivations ranging from molecular chemistry to social networks.

This book constitutes the proceedings of the 21st International Conference on Discovery Science, DS 2018, held in Limassol, Cyprus, in October 2018, co-located with the International Symposium on Methodologies for Intelligent Systems, ISMIS 2018. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefully reviewed and selected from 71 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Classification; meta-learning; reinforcement learning; streams and time series; subgroup and subgraph discovery; text mining; and applications.