Brazdil, P., and Clark, P. : "Learning from Imperfect Data", in Proceedings of International Workshop on Machine Learning, Meta Reasoning and Logics, Sesimbra, Portugal, 1988. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Knowledge Integration And Forgetting - Torgo, Kubat (1991) (Correct)
....learning purposes and a testing set which is used to evaluate the learned theory. Accuracy is then calculated as the percentage of testing examples that are correctly classified by the learned theory. The algorithm should be robust in the sense that it should cope with the various forms of noise [Brazdil Clark,1988], such as wrong attribute values, incorrect pre classification of the examples given to the algorithm, etc. Simplicity can be expressed in terms of the number of rules (or tree branches) and the average length of the rules (or the average number of nodes of a path from the root to a leaf in the ....
Brazdil, P., and Clark, P. : "Learning from Imperfect Data", in Proceedings of International Workshop on Machine Learning, Meta Reasoning and Logics, Sesimbra, Portugal, 1988.
Knowledge Integration and Learning - Brazdil, Torgo (1991) Self-citation (Brazdil) (Correct)
....of the paper is organized as follows. Section 2 describes the basic method of knowledge integration, including how we assess rule quality. Section 3 describes the results of our experiments with INTEG3.1. This system is an enhanced version of INTEG.3 that has been described in an earlier paper [Brazdil and Torgo, 1990]. Section 4 discusses some other alternative methods of evaluating rule quality. This section is followed by a general discussion and conclusions. 2. Method of Knowledge Integration In this section we will describe the method of knowledge integration in more detail. Basically, the process ....
....possibility we have decided to check whether the individual systems have a satisfactory performance, when compared to other systems reported in literature. We have chosen two systems (S 1 , S 3 ) and these were were supplied all the data available for 3 In the first series of test reported in [Brazdil and Torgo, 1990] the performance gains were somewhat higher than the ones given here. Our previous results were not quite right, however. The problem was caused by a testing method. However, the differences between the two sets of results are not too large. training. These experiments were repeated 20 times. ....
[Article contains additional citation context not shown here]
Brazdil, P. and Clark, P. (1990): "Learning from Imperfect Data", in Machine Learning, Meta-Reasoning and Logics, P. Brazdil and K.Konolige (eds.), Kluwer Academic Publishers.
Knowledge Acquisition via Knowledge Integration - Brazdil, Torgo (1990) (10 citations) Self-citation (Brazdil) (Correct)
No context found.
Brazdil, P. and Clark, P. (1990): "Learning from Imperfect Data", in Machine Learning, MetaReasoning and Logics, P. Bradzil and K. Konolige (eds.) Kluwer Academic Publishers.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC