Data collection and annotation is a laborious, time-consuming prerequisite
for supervised machine learning tasks. Online Active Learning (OAL) is a
paradigm that addresses this issue by simultaneously minimizing the amount of
annotation required to train a classifier and adapting to changes in the data
over the duration of the data collection process. Prior work has indicated that
fluctuating class distributions and data drift are still common problems for
OAL. This work presents new loss functions that address these challenges when
OAL is applied to Sound Event Detection (SED). Experimental results from the
SONYC dataset and two Voice-Type Discrimination (VTD) corpora indicate that OAL
can reduce the time and effort required to train SED classifiers by a factor of
5 for SONYC, and that the new methods presented here successfully resolve
issues present in existing OAL methods.

在线主动学习（OAL）应用于声音事件检测（SED）时，本研究提出了能够解决 OAL 中存在的挑战的新型损失函数，实验结果表明 OAL 可将训练 SED 分类器所需的时间和精力减少 5 倍，并成功解决现有 OAL 方法存在的问题。

在线声音事件检测的主动学习

Online Active Learning For Sound Event Detection

We study {\em online} active learning of homogeneous halfspaces in
$\mathbb{R}^d$ with adversarial noise where the overall probability of a noisy
label is constrained to be at most $\nu$. Our main contribution is a
Perceptron-like online active learning algorithm that runs in polynomial time,
and under the conditions that the marginal distribution is isotropic
log-concave and $\nu = \Omega(\epsilon)$, where $\epsilon \in (0, 1)$ is the
target error rate, our algorithm PAC learns the underlying halfspace with
near-optimal label complexity of $\tilde{O}\big(d \cdot
polylog(\frac{1}{\epsilon})\big)$ and sample complexity of
$\tilde{O}\big(\frac{d}{\epsilon} \big)$. Prior to this work, existing online
algorithms designed for tolerating the adversarial noise are subject to either
label complexity polynomial in $\frac{1}{\epsilon}$, or suboptimal noise
tolerance, or restrictive marginal distributions. With the additional prior
knowledge that the underlying halfspace is $s$-sparse, we obtain
attribute-efficient label complexity of $\tilde{O}\big( s \cdot polylog(d,
\frac{1}{\epsilon}) \big)$ and sample complexity of
$\tilde{O}\big(\frac{s}{\epsilon} \cdot polylog(d) \big)$. As an immediate
corollary, we show that under the agnostic model where no assumption is made on
the noise rate $\nu$, our active learner achieves an error rate of $O(OPT) +
\epsilon$ with the same running time and label and sample complexity, where
$OPT$ is the best possible error rate achievable by any homogeneous halfspace.

本文介绍了一种 Perceptron-like 在线主动学习算法，能够以近乎最优的标签复杂度和样本复杂度下，学习噪声容限在总概率最多为 ν，样本标签误差率 ε 和输入维度 d 给定的 R^d 中同质半空间。该算法的先前版本针对噪声容限存在的情况，不能同时获得标签和样本复杂度多项式级别的优良性能。经过一定的假设前提下，支持 5 罕见的瑕疵。

关于局部感知机具有优势的半空间对抗噪声最优学习

On the Power of Localized Perceptron for Label-Optimal Learning of  Halfspaces with Adversarial Noise

We consider the problem of online active learning to collect data for
regression modeling. Specifically, we consider a decision maker with a limited
experimentation budget who must efficiently learn an underlying linear
population model. Our main contribution is a novel threshold-based algorithm
for selection of most informative observations; we characterize its performance
and fundamental lower bounds. We extend the algorithm and its guarantees to
sparse linear regression in high-dimensional settings. Simulations suggest the
algorithm is remarkably robust: it provides significant benefits over passive
random sampling in real-world datasets that exhibit high nonlinearity and high
dimensionality --- significantly reducing both the mean and variance of the
squared error.

该研究考虑在线主动学习来收集回归建模的数据，提出了一种基于阈值的算法来选择最具信息量的观测数据，并将其推广到高维稀疏线性回归，仿真结果表明该算法相对于被动随机抽样具有显著优势。