We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
This case study compares students' learning experience and outcomes in the lecture and online versions of a first-year introductory computing course offered at the Department...
In this paper a learning based local search approach for propositional satisfiability is presented. It is based on an original adaptation of the conflict driven clause learning ...
Gilles Audemard, Jean-Marie Lagniez, Bertrand Mazu...
A model-free, case-based learning and control algorithm called S-learning is described as implemented in a simulation of a light-seeking mobile robot. S-learning demonstrated learn...
It is difficult to learn good classifiers when training data is missing attribute values. Conventional techniques for dealing with such omissions, such as mean imputation, general...
Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greine...