The Averaged One Dependency Estimator (AODE) is integrated all possible Super-Parent-One-Dependency Estimators (SPODEs) and estimates class conditional probabilities by averaging them. In an AODE network some redundant SPODEs maybe result in some bias of classifiers, as a consequence, it could reduce the classification accuracy substantially. In this paper, a kind of MDL metrics is used to sele...

This paper stands in the context of reinforcement learning with partial observability and limited data. In this setting, we focus on the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data), and theoretically show that while potentially increasing the asymptotic bias, a smaller state representation decreases the risk...

It is acknowledged that overfitting can occur in feature selection using the wrapper method when there is a limited amount of training data available. It has also been shown that the severity of overfitting is related to the intensity of the search algorithm used during this process. We demonstrate that the problem of overfitting in feature weighting can be exacerbated if the feature weighting ...

Machine learning is a powerful set of techniques that has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, MEG, and PET data. With these new techniques come new dangers of overfitting that are not well understood by the neuroscience community. In this article, we use Support Vector Machine (SVM) classifiers, and genetic algorithms to demonstrate th...

We propose an extension to the notion of one-mode projection, for the case of temporal bipartite graphs. Through a Bayesian iterative update scheme, our method produces an estimate of the one-mode network at each step, by describing each link via probability distributions over i) its presence/absence and ii) weight. Our approach models the statistical significance of each link in the projected ...

In mathematical finance, backtest overfitting connotes the usage of historical market data to develop an investment strategy, where too many variations of the strategy are tried, relative to the amount of data available. Backtest overfitting is now thought to be a primary reason why investment models and strategies that look good on paper often disappoint in practice. Models and strategies suff...

Prism is a modular classification rule generation method based on the ‘separate and conquer’ approach that is alternative to the rule induction approach using decision trees also known as ‘divide and conquer’. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other...

When fitting an econometric model, it is well known that we pick up part of the idiosyncratic characteristics of the data along with the systematic relationship between dependent and explanatory variables. This phenomenon is known as overfitting and generally occurs when a model is excessively complex relative to the amount of data available. Overfitting is a major threat to regression analysis...

Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented training set, leading poor generalization. this study, we provide new insights on problem of overfitting under class by inspecting network behavior. We fi...

Conventional approaches to voice conversion typically use a GMM to represent the joint probability density of source and target features. This model is then used to perform spectral conversion between speakers. This approach is reasonably effective but can be prone to overfitting and oversmoothing of the target spectra. This paper proposes an alternative scheme that uses a collection of Gaussia...