Hang Li – författare

Learning to rank refers to machine learning techniques for training a model in a ranking task. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

This book constitutes the thoroughly refereed post-conference proceedings of the 4th Asia Information Retrieval Symposium, AIRS 2008, held in Harbin, China, in May 2008. The 39 revised full papers and 43 revised poster papers presented were carefully reviewed and selected from 144 submissions. All current issues in information retrieval are addressed: applications, systems, technologies and theoretical aspects of information retrieval in text, audio, image, video and multi-media data. The papers are organized in topical sections on IR models image retrieval, text classification, chinese language processing, text processing, application of IR, machine learning, taxonomy, IR methods, information extraction, summarization, multimedia, Web IR, and text clustering.

This book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China in May 2007. The 34 revised full papers and 92 revised short papers presented together with 4 keynote talks or extended abstracts thereof were carefully reviewed and selected from 730 submissions. The papers are devoted to new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, databases, statistics, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.

This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods.