Geoff Holmes

Bernahrd Pfahringer

Achim Hoffman

The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R & D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes proceedings (published in time for the respective conference) post-proceedings (consisting of thoroughly revised final full papers) research monographs (which may be based on PhD work) More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components; these sublines include tutorials (textbook-like monographs or collections of lectures given at advance courses) state-of-the-art surveys (offering complete and mediated coverage of a topic) hot topics (introducing emergent topics to the broader community) In parallel to the printed book, each new volume is published electronically in LNCS Online. Book jacket.

13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010, Proceedings

Discovery Science

Min-Ling Zhang

Kevin Knight

Changshui Zhang

This book constitutes the refereed proceedings of the Second CCF International Conference on Artificial Intelligence, CCF-ICAI 2019, held in Xuzhou, China in August, 2019. The 23 papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on ​deep learning, image and video processing, NLP and recommender system, machine learning algorithms, and AI applications.

Second CCF International Conference, ICAI 2019, Xuzhou, China, August 22-23, 2019, Proceedings

Artificial Intelligence

Albert Bifet

Bernhard Pfahringer

Ricard Gavalda

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

with Practical Examples in MOA

Machine Learning for Data Streams

Geoff Holmes

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