A perceptual system coping with a dynamic environment must be able to learn to detect new object categories from a few examples. However, learning from a small sample is restricted by the hindering effects of model overfitting. We present an algorithm aimed at circumventing the effects of overfitting by utilizing a set of reusable features, learned from several previously trained categories. We...

The aim of this Letter to the Editor was to report some methodological shortcomings in a recently published article. Issues regarding missing values and overfitting are mentioned. First, Complete Case (CC) analysis was used instead of an imputation method. Second, there was a high chance of overfitting and lack of model validation. In conclusion, the results of this study should be interpret wi...

This project examines overfitting and generalization error with Monte Carlo Tree Search in the real-time strategy video game StarCraft and provides a basis for evaluating the specific scenarios of StarCraft based on their estimated generalization error. How can we tell if we are overfitting the input parameters to specific scenarios in StarCraft? How well does a specific scenario of StarCraft g...

Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the ...

Word alignment is the basis of statistical machine translation. GIZA++ is a popular tool for producing word alignments and translation models. It uses a set of parameters that affect the quality of word alignments and translation models. These parameters exist to overcome some problems such as overfitting. This paper addresses the problem of tuning GIZA++ parameter for better translation qualit...

Multilayer perceptrons (MLP) with one hidden layer have been used for a long time to deal with non-linear regression. However, in some task, MLP’s are too powerful models and a small mean square error (MSE) may be more due to overfitting than to actual modelling. If the noise of the regression model is Gaussian, the overfitting of the model is totally determined by the behavior of the likelihoo...

Recent computational advances allow investment managers to search for profitable investment strategies. In many instances, that search involves a pseudo-mathematical argument, which is spuriously validated through a simulation of its historical performance (also called backtest). We prove that high performance is easily achievable after backtesting a relatively small number of alternative strat...