Learning relations from noisy examples: An empirical comparison of LINUS and FOIL - Dzeroski, Lavrac - 1991 () Citation Context ...Serge, & Koppel, 1991; Thompson et al., 1991; Cohen, 1992; Pazzani & Kibler, 199
Robust imitation learning from noisy demonstrations[J]. arXiv preprint arXiv:2010.10181, 2020. Motivation 模仿学习经常遇到因为轨迹质量良莠不齐而导致的鲁棒性问题,通常的解决方案需要引入额外的标注来解决,本文提出一种仅利用数据本身进行轨迹质量判别的逆向强化学习方案。 Assumptions 本文基于几个重要假设来进行...
Learning from noisy examples Mach. Learn. (1988) B.Frénayet al. Classification in the presence of label noise: a survey IEEE Trans. Neural Netw. Learn.Syst. (2013) J.Zhanget al. Learning saliency from single noisy labelling: arobust model fitting perspective ...
Even at the 50% noise level when all of the INCREMENTAL LEARNING FROMNOISY DATA 339 Figure5. STAGGER'S differential noise tolerance. positive instances are randomly designated as examples or nonexamples, STAGGER is still able to utilize the information present in the negative instances to form an...
There is a target to be learned, but it’s unknown to us. We have a set of examples generated by the target. The learning algorithm uses these examples to look for a hypothesis that approximates the target. 一个简单的学习模型 对于一个实际的学习问题来说, 期望的目标函数f和数据集X能通过...
Social learning enables complex societies. However, it is largely unknown how insights obtained from observation compare with insights gained from trial-and-error, in particular in terms of their robustness. Here, we use aversive reinforcement to train
相反,Logic programming比如Inductive Logic Programming用很少数据训练后就能推理,但是Logic Programming对 噪声的加入 和 数据的错误标注 没有鲁棒性,更严重的是,Logic Programming不能应用到 数据歧义的 非符号 领域 比如 raw像素。 这篇文章提出Differentiable Inductive Logic framework可以有Logic Programming的特性(少量...
Future work may leverage recent advances in deep learning from noisy annotations40. Meanwhile, even though the chances do get better with larger sets of correctly labelled data, no dataset can possibly cover absolutely all-different variants of hemorrhagic stroke, whilst the theoretical ‘ground-truth...
Examples and use cases Exploratory analysis and dimensionality reduction are two of the most common uses for unsupervised learning. Exploratory analysis, which uses algorithms to detect patterns that were previously unknown, has a range of real-world enterprise applications. For example, businesses canus...
Machine learning employs a variety of statistical, probabilistic, fuzzy and optimization techniques that allow computers to “learn” from examples and to detect hard-to-discern patterns from large, noisy or complex datasets. This capability is well-suited to medical applications, and machine learning...