Machine Learning Proceedings 1994. Proceedings of the by Author Unknown

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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

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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

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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.

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