Generalization performance, also known as out-of-sample performance, refers to how well a machine learning model performs on unseen data and is a key consideration in model selection.

Jan 22, 2025 · Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way …

May 18, 2021 · This theory explains various generalization phenomena observed in wide neural networks, which admit a kernel limit and generalize well despite being overparameterized.

Fortunately, there's a very convenient way to measure an algorithm's generalization performance: we measure its performance on a held-out test set, consisting of examples it hasn't seen before.

Sep 30, 2022 · Given that the number of samples is fixed, to improve generalization in learning algorithms and to avoid overfitting, researchers over time have built numerous mechanisms into the …

Sep 26, 2024 · Generalization performance refers to how well a model can make accurate predictions on new, unseen data. A model with high generalization performance is not only effective with training …

In this paper, works related to explanations of neural networks’ generalization performance are reviewed. In the first part, recent progress in calculating tight generalization bounds is separated into …

Simply put, generalization relates the performance of a model on seen examples to its performance on unseen examples. In this chapter, we discuss the interplay between representation, optimization, and …

In this paper, we analyze the distributions of generalization performance on the learning curves. We compile a high-fidelity learning curve database, both with respect to training set size and repetitions …

Ensemble clustering has demonstrated great success in practice; however, its theoretical foundations remain underexplored. This paper examines the generalization performance of ensemble clustering, …