Discovery science

Autor*innen

• João Gama

Parameter

• 474 Seiten
• 17 Lesestunden

• Computer & Internet

Mehr zum Buch

This collection covers a wide range of topics in inference and learning within planning, exploring various methodologies and applications. It discusses mining heterogeneous information networks, the intricacies of learning on the web, and the dual aspects of active learning. The text presents an iterative learning algorithm for within-network regression and addresses the detection of new patient safety incidents. It also examines data mining techniques for wine quality assessment and introduces MICCLLR for multiple-instance learning. Further, it delves into the complexity of constraint-based theory extraction and features algorithm selection for vegetation condition prediction. The book highlights regression trees from data streams, frequent bipartite episode mining, and a two-stage density-based clustering algorithm for dynamic networks. It covers large margin decision lists for multi-class classification, centrality measures in active learning, and player modeling for intelligent difficulty adjustment. Additionally, it discusses unsupervised fuzzy clustering for sea surface temperature images, community detection algorithms, and the development of an ontology for data mining investigations. The text explores online mass flow prediction, domain knowledge in data streams, influential nodes in social networks, and an empirical comparison of probability estimation techniques. It also addresses precision and recall for regressi

• Sprache: Englisch
• Erscheinungsdatum: 2009
• Einband: Paperback