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Provides forty two papers from the July 1994 convention. themes coated contain enhancing accuracy of flawed area theories, grasping characteristic choice, boosting and different computer studying algorithms, incremental reduced-error pruning, studying disjunctive options utilizing genetic algorithms, and a Baye
Read or Download Machine Learning Proceedings 1994. Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, July 10–13, 1994 PDF
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Extra resources for Machine Learning Proceedings 1994. Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, July 10–13, 1994
Another difference lies in the use of least general generalization as a technique t o create generalized descriptions of such groups. Improving Accuracy of Incorrect Domain Theories Acknowledgements T h e work presented in this paper has benefitted greatly from discussions with a number of people. I want t o t h a n k Carl Gustaf Jansson, P a t Langley, Steve Minton, Ray Mooney and Ross Quinlan for invaluable suggestions and comments. Furthermore I would like t o t h a n k Malini B h a n d a r u , Henrik Bostrom, William Cohen, J o n Gratch, Peter Idestam-Almquist, Mike Pazzani, Christer Samuelsson, Alberto Segre and the anonymous referees for carefully reading and commenting on earlier drafts of this paper.
Enrolled(Student). deferment(Student). school(Student), removed % ======== enrolled. n_units (Student, 5 ) . - Figure 6 shows one example of the resulting theory. In this run GENTRE had been given 40 training examples. 65 % accuracy on all the unseen examples. eligible_for_deferment(Student) :mil it ary_def erment (Student). eligible_for_deferment(Student) :peace_corps_deferment(Student). deferment(Student). eligible_for_deferment(Student) :student_deferment(Student). deferment(Student). forces(Org).
Even partial mitigation yields substantial improvements in generalization performance. For example, restricting the attributes learning is allowed to consider more than doubles generalization performance on the DAY-OF-WEEK task. 3 ATTRIBUTE HILLCLIMBING This section describes five hillclimbing methods that greedily search for attribute subsets that generalize well when given to a learning procedure. The methods differ only in the particular hillclimbing strategy they employ. 1 Performance Criterion To do hillclimbing, we first need a metric to define the hills.