Jiuyong Li - Böcker

They are organized in topical sections on machine learning, deep learning, multi-agent systems, neural computing and swarm intelligence, natural language processing, recommendation systems, social computing, business intelligence and security, pattern recognition, and image understanding.

They are organized in topical sections on machine learning, deep learning, multi-agent systems, neural computing and swarm intelligence, natural language processing, recommendation systems, social computing, business intelligence and security, pattern recognition, and image understanding.

This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.

This book constitutes the refereed proceedings of the 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010, held in Adelaide, Australia, in December 2010. The 52 revised full papers presented were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on knowledge representation and reasoning; data mining and knowledge discovery; machine learning; statistical learning; evolutionary computation; particle swarm optimization; intelligent agent; search and planning; natural language processing; and AI applications.