Scha#er, Cullen. 1993a. Overfitting avoidance as bias. Machine Learning, 10:153--178. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
A Cognitive Bias Approach to Feature Selection and Weighting for.. - Cardie (Correct)
....also reflect a potential tradeo# between the quality of the selected bias combination and the computing time required to make the selection. 6.1. The Bias Selection Algorithms At a high level, both the greedy and exhaustive bias selection methods employ nested cross validation as suggested in Scha#er (1993) and outlined in Figure 2. 18 CLAIRE CARDIE Greedy ( Biases, Learning Data, Selection Data ) 1. Initializations: 2. Available Biases = Biases 3. Sel Biases = biases selected thus far 4. Sel Biases Acc = 0 accuracy of Sel Biases 5. While Available Biases 6. Best Bias = ....
Scha#er, C. (1993). Overfitting avoidance as bias. Machine Learning, 10 (2), 153--178.
E4 - Machine Learning - Domingos (Correct)
....no general purpose learning method; each method s utility is contingent on the assumptions it makes, and each application requires individual attention. Universal laws of discovery like simple hypotheses are more accurate (sometimes known as Occam s razor ) should be viewed with suspicion (Scha#er, 1993; Webb, 1996; Domingos, 1998) Having made the notion of bias explicit, machine learning has gone on to study the changes in bias (Gordon desJardins, 1995) and combinations of di#erent biases (Michalski Wnek, 1996) that are often required for practical success. Awareness of the importance of ....
Scha#er, C. (1993). Overfitting avoidance as bias. Machine Learning, 10, 153-178.
The Role of Occam's Razor in Knowledge Discovery - Domingos (1999) (14 citations) (Correct)
....pedrod cs.washington.edu Department of Computer Science and Engineering University of Washington Seattle, WA 98195 Abstract. Many KDD systems incorporate an implicit or explicit preference for simpler models, but this use of Occam s razor has been strongly criticized by several authors (e.g. Scha#er, 1993; Webb, 1996) This controversy arises partly because Occam s razor has been interpreted in two quite di#erent ways. The first interpretation (simplicity is a goal in itself) is essentially correct, but is at heart a preference for more comprehensible models. The second interpretation (simplicity ....
....is indeed the case. 4.1. Pruning A simple empirical argument for the second razor might be stated as Pruning works. Indeed, pruning often leads to models that are both simpler and more accurate than the corresponding unpruned ones (Mingers, 1989) However, it can also lead to lower accuracy (Scha#er, 1993). It is easy to think of simple problems where pruning can only hurt accuracy (e.g. applying a decision tree algorithm like C4.5 to learning a noise free, diagonal frontier) More importantly, as mentioned above, Cohen and Jensen (1999) have shown persuasively that pruning should not be seen as a ....
[Article contains additional citation context not shown here]
Scha#er, C. (1993). Overfitting avoidance as bias. Machine Learning, 10, 153--178.
Active Learning with Multiple Views - Muslea (2002) (4 citations) (Correct)
No context found.
Scha#er, Cullen. 1993a. Overfitting avoidance as bias. Machine Learning, 10:153--178.
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