In this paper, we consider the effect of feature noise in active learning, which could arise either because X X X itself is being measured, or it is corrupted in transmission to the oracle, or the oracle returns the label of a noisy version of the query point. In statistics, feature ...
In this paper, we therefore study active learning with such human-like oracles, by making a more realistic assumption that the noise is example-dependent (i.e., non-uniformly distributed over the sample space). More specifically, when the human-like oracle is highly confident in labelling ...
In this paper, we introduce the new problem of re- active learning, a generalization of active learning in which we seek to understand the difference in marginal value be- tween decreasing the noise of a training set, via relabeling... CH Lin,Mausam,DS Weld - International Conference on Icm...
We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise. We show that when the imposed noise satisfies the Tsybakov low noise condition (Mammen, Tsybakov, ...
Deep Active Learning in the Presence of Label Noise: A Survey Link Active Learning with Neural Networks: Insights from Nonparametric Statistics Towards Robust Deep Active Learning for Scientific Computing Deep active learning for object detection Deep Active Learning for Axon-Myelin Segmentation on Histolo...
We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to i... D Golovin,A Krause,D Ray - International Conference on Neural Information Processing Systems 被引量: 196发表: 2010年 Bayesian approaches...
This paper presents a new solution for Active Noise Control problem based on Q-Learning algorithm. This feedback method, needs no information about primary and secondary transfer functions and it is fully robust to subsystem dynamics changes. It is shown through simulation that the proposed method ...
Langford. Agnostic active learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 65–72. ACM Press, 2006. 主要介绍了一个应该还是很有名的算法: A2 (Agnostic active algorithm)。号称是第一个AA算法。 agnostic对应的是realizable case,区别在于考虑的hypothesis class是否...
Introduction to Sound Programming with ALSA A Tutorial on Using the ALSA Audio API Rpi I2S thread Implementation of FIR Filtering in C Included third-party libraries This software uses matplotlibcpp - see matplotlibcpp-license.txt About Active Noise Control on Raspberry Pi ...
Active noise canceling is demonstrated by analog neuro-chips with on-chip learning capability. The developed neuro-chip faithfully incorporates the error-backpropagation learning rule. Without any digital signal processor the developed system successfully compensated for nonlinear distortion of loudspeakers as...