TL;DR
The results demonstrate that autokRF is a scalability-oriented framework for large-scale classification that automatically tunes key hyper-parammeters, eliminating the complex and time-consuming manual tuning process required from data scientists.
Gradient boosting decision trees (GBDTs) are highly effective for tabular data, but their data-dependent and irregular tree construction limits mini-batch training, incremental updates, and efficient GPU execution. We present TemplateGBM, a GBDT learning framework that separates tree-structure selection from leaf-weigh...
Evaluating the performance of Greedy K-center across a variety of metric spaces shows that mapping unlabeled instances into a predictive probability space and weighting the result by entropy often dominates the other options for active learning selection with Greedy K-center.
This paper proposes a meta-learning framework that leverages a comprehensive set of meta-features capturing dataset complexity to predict classifier performance without exhaustive training, and achieves an average ranking prediction accuracy exceeding 86%, demonstrating its effectiveness in guiding model selection.
The proposed meta-adaptive resampling selection plus plus (MARS) is a stability aware meta-adaptive framework that chooses the best method based on the data based on the data, suggesting that selecting methods based on stability worked well on the evaluated datasets.
Background/Aim: In the classification of remote sensing data, Machine Learning (ML) models when used alone fall short in capturing spatial and spectral features. These models, especially in the classification of hyperspectral images with similar spectral characteristics, produce high generalization errors. In this stud...
Accurate optimization of a supervised spectral objective need not produce an accurate population subspace or a better predictive representation. We investigate these distinctions for Online Kernel Supervised Principal Component Analysis (OKSPCA), which combines a centered cross-moment in finite random-feature coordinat...
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