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	Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Learning (I.2.6) > Parameter Learning (I.2.6...)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-7 of 7 Reviews about "Parameter Learning (I.2.6...)": Date Reviewed   Learning Bayesian network parameters from small data sets Guo Z., Gao X., Ren H., Yang Y., Di R., Chen D. International Journal of Approximate Reasoning 91 22-35, 2017.  Type: Article Bayesian networks (BNs) represent a powerful statistical tool for uncertainty analysis with applications in many areas, for example, medical diagnosis. Since data is often not sufficiently available to accurately learn the parameters o... Mar 12 2018   Context-aware item-to-item recommendation within the factorization framework Hidasi B., Tikk D.  CaRR 2013 (Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation, Rome, Italy, Feb 5, 2013) 19-25, 2013.  Type: Proceedings This paper considers a number of ways to introduce context into recommender systems. Specifically, it considers the factorization framework, in which a low-rank vector is computed for each user and each item. Preferences can be approxi... Oct 1 2014   Multi-parametric solution-path algorithm for instance-weighted support vector machines Karasuyama M., Harada N., Sugiyama M., Takeuchi I. Machine Learning 88(3): 297-330, 2012.  Type: Article In a weighted support vector machine (WSVM), each training instance has its own weight. So, for example, in nonstationary data analysis, earlier instances may have less weight than later ones, or in heteroscedastic data modeling, large... Nov 15 2012   Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaigns Dereszynski E., Dietterich T. ACM Transactions on Sensor Networks 8(1): 1-36, 2011.  Type: Article Portable sensor stations allow for the transport of equipment to sites of interest and make it possible to observe ecological phenomena at any desired spatial granularity. In addition, these networks operate at fine time resolution, th... Jan 11 2012    Probabilistic graphical models: principles and techniques Koller D., Friedman N., The MIT Press, Cambridge, MA, 2009. 1208 pp.  Type: Book (978-0-262013-19-2) Efforts over the past 60 years to use computers to implement human-like reasoning have favored the interpretation of probabilities as reflecting degrees of belief, fueling the rapid growth of Bayesian formalisms. While theoretically at... Oct 6 2010   An adjustment model in a geometric constraint solving problem Pavón R., Díaz F., Luzón M.  Applied computing (Proceedings of the 2006 ACM Symposium on Applied Computing, Dijon, France, Apr 23-27, 2006) 968-973, 2006.  Type: Proceedings A common problem in computer-aided design (CAD) is that of constraint-based geometric design. Typically, one wishes to place a number of points in space subject to a given set of constraints, where the number of constraints is sufficie... Aug 31 2006   Learning and Classification of Complex Dynamics North B., Blake A., Isard M., Rittscher J. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(9): 1016-1034, 2000.  Type: Article The authors have opened a new window on the problem of learning process and learning theory in artificial intelligence (AI) systems, including classification in a complex, dynamic environment. It has been extended by them with the use ... Dec 1 2001       Display 510152550100 per page
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