Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a “partial” approach to improve the...

The automatic induction of classification rules from examples is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. This paper describes a means of reducing overfitting known as J-pruning, based on the J-measure, an information theoretic means of quantifying the information content of a rule, and examines its effectiveness i...

This article is concerned with a nonparametric regression problem in which the input variables and errors are autocorrelated time. The motivation for research stems from modeling wind power curves. Using existing model selection methods, like cross-validation, results overfitting presence of temporal autocorrelation. phenomenon referred to as overfitting, causes loss performance while predictin...

Automatic program repair (APR) aims to reduce the cost of manually fixing software defects. However, APR suffers from generating a multitude overfitting patches, those patches that fail correctly defect beyond making tests pass. This paper presents novel patch detection system called ODS assess correctness patches. first statically compares patched and buggy in order extract code features at ab...

The fast-growing field of Computational Cognitive Neuroscience is on track to meet its first crisis. A large number papers in this nascent are developing and testing novel analysis methods using the same stimuli neuroimaging datasets. Publication bias confirmatory exploration will result overfitting limited available data. urgently needs collect more good quality open data a variety experimenta...

The symmetric EM algorithm is proposed for probabilistic latent semantic analysis in collaborative filtering. The algorithm allows to reveal the latent interest profiles of both users and items, then to easily construct high-quality similarity measures of all required types: user–user, item–item, and item–user. The advantage of the proposed approach is that different profiles are consistent to ...

In this paper we examine a perceptron learning task. The task is realizable since it is provided by another perceptron with identical architecture. Both perceptrons have nonlinear sigmoid output functions. The gain of the output function determines the level of nonlinearity of the learning task. It is observed that a high level of nonlinearity leads to overfitting. We give an explanation for th...