Suppose that, for a learning task, we have to select one hypothesis out of a set of hypotheses (that may, for example, have been generated by multiple applications of a randomized learning algorithm). A common approach is to evaluate each hypothesis in the set on some previously unseen cross-validation data, and then to select the hypothesis that had the lowest cross-validation error. But when ...

Applying quantum processors to model a high-dimensional function approximator is typical method in machine learning with potential advantage. It conjectured that the unitarity of circuits provides possible regularization avoid overfitting. However, it not clear how interplays expressibility under limitation current Noisy-Intermediate Scale Quantum devices. In this article, we perform simulation...

Quantitative investment strategies are often selected from a broad class of candidate models estimated and tested on historical data. Standard statistical techniques to prevent model overfitting such as out-sample backtesting turn out be unreliable in situations when the selection is based results too many holdout sample. There an ongoing discussion how estimate probability backtest adjust expe...

For many reasons, neural networks have become very popular AI machine learning models. Two of the most important aspects of machine learning models are how well the model generalizes to unseen data, and how well the model scales with problem complexity. Using a controlled task with known optimal training error, we investigate the convergence of the backpropagation (BP) algorithm. We find that t...

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...

• We propose a novel resistance loss to alleviate model overfitting framework robustly train CNNs on noisy labels It integrates CNNs’ memorization effect, curriculum learning with resis-tance Noisy composed of correct and corrupted ones are pervasive in practice. They might significantly deteriorate the performance convolutional neural networks (CNNs), because easily overfitted labels. To addre...

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 ...