منابع مشابه
On Overfitting Avoidance as Bias
In supervised learning it is commonly believed that penalizing complex functions helps one avoid "overfitting" functions to data, and therefore improves generalization. It is also commonly believed that cross-validation is an effective way to choose amongst algorithms for fitting functions to data. In a recent paper, Schaffer (1993) presents experimental evidence disputing these claims. The cur...
متن کاملStacked Training for Overfitting Avoidance in Deep Networks
When training deep networks and other complex networks of predictors, the risk of overfitting is typically of large concern. We examine the use of stacking, a method for training multiple simultaneous predictors in order to simulate the overfitting in early layers of a network, and show how to utilize this approach for both forward training and backpropagation learning in deep networks. We then...
متن کاملComprehensibility & Overfitting Avoidance in Genetic Programming for Technical Trading Rules
This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehens...
متن کاملOverfitting Avoidance for Stochastic Modeling of Attribute-Value Grammars
We present a novel approach to the problem of overfitting in the training of stochastic models for selecting parses generated by attributevalued grammars. In this approach, statistical features are merged according to the frequency of linguistic elements within the features. The resulting models are more general than the original models, and contain fewer parameters. Empirical results from the ...
متن کاملSparse Data and the Effect of Overfitting Avoidance in Decision Tree Induction
the training data itself to tell us whether it is sparse enough to make unpruned trees preferable. As argued at length in Schaaer, 1992b], training data cannot tell us what bias is appropriate to use in interpreting it. In particular, the sparsity of data depends on the complexity of the true relationship underlying data generation; and it is not data but domain knowledge that can tell us how c...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1993
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00993504