Journal Article10.1109/69.494161
A guide to the literature on learning probabilistic networks from data
TL;DR: The literature review presented discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks.
read more
Abstract: The literature review presented discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The article avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
Nir Friedman,Daphne Koller +1 more
TL;DR: This paper shows how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables, and uses this result as the basis for an algorithm that approximates the Bayesian posterior of a feature.
Learning Bayesian networks from data: an information-theory based approach
TL;DR: Algorithms that use an information-theoretic analysis to learn Bayesian network structures from data, requiring only polynomial numbers of conditional independence tests in typical cases are provided.
882
Predictive data mining in clinical medicine: Current issues and guidelines
TL;DR: The extent and role of the research area of predictive data mining and a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine are discussed and proposed.
872
Transfer learning using computational intelligence
TL;DR: This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories and provides state-of-the-art knowledge that will directly support researchers and practice-based professionals to understand the developments in computational Intelligence- based transfer learning research and applications.
Bayesian Networks for Data Mining
TL;DR: Methods for constructing Bayesian networks from prior knowledge are discussed and Bayesian statistical methods for using data to improve these models are summarized.
References
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
•Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
- 01 Jan 1988
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
17.6K
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
David E. Rumelhart,James L. McClelland,Au +2 more
- 17 Jul 1986
TL;DR: The fundamental principles, basic mechanisms, and formal analyses involved in the development of parallel distributed processing (PDP) systems are presented in individual chapters contributed by leading experts.
16.7K