arXiv：cs.LG· Carl Myers Kadie·· 3 小时前AI 评分16
Seer：用最大似然回归建模学习速度曲线
Seer: Maximum Likelihood Regression for Learning-Speed Curves
AI 导读
Seer 是一个通过生成分类学习性能的经验观测、再据此建立统计模型来预测学习表现的系统，可预测达到目标精度所需的训练样本数以及样本无限时的最高精度。它提出更优约束的模型与高效的最大似然求解算法，并在大豆病害、心脏病和听力学三个领域的近 100 项实验中验证了建模效果。该工作为 1995 年伊利诺伊大学厄巴纳-香槟分校博士论文，共 104 页。
正文
Abstract:The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning performance and then uses those observations to create statistical models. The models can be used to predict the number of training examples needed to achieve a desired level and the maximum accuracy possible given an unlimited number of training examples. Seer advances the state of the art with 1) models that embody the best constraints for classification learning and most useful parameters, 2) algorithms that efficiently find maximum-likelihood models, and 3) a demonstration on real-world data from three domains of a practicable application of such modeling.
The first part of the thesis gives an overview of the requirements for a good maximum-likelihood model of classification-learning performance. Next, reasonable design choices for such models are explored. Selection among such models is a task of nonlinear programming, but by exploiting appropriate problem constraints, the task is reduced to a nonlinear regression task that can be solved with an efficient iterative algorithm. The latter part of the thesis describes almost 100 experiments in the domains of soybean disease, heart disease, and audiological problems. The tests show that Seer is excellent at characterizing learning-performance and that it seems to be as good as possible at predicting learning performance. Finally, recommendations for choosing a regression model for a particular situation are made and directions for further research are identified.
| Comments: | 104 pages. Ph.D. dissertation, Department of Computer Science, University of Illinois at Urbana-Champaign, 1995. Original dissertation deposited in arXiv in 2026 |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | I.2.6 |
| Report number: | UILU-ENG 95 1727 |
| Cite as: | arXiv:2610.02610 [cs.LG] |
| (or arXiv:2610.02610v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02610 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Carl Kadie [view email]
[v1]
Fri, 2 Oct 2026 00:09:10 UTC (643 KB)
来源：arXiv：cs.LG · arxiv.org