Active Learning (Synthesis Lectures on Artificial Intelligence and Machine Le)

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain.

This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.

Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations

主動學習的關鍵理念是，如果機器學習演算法被允許選擇它所學習的數據，那麼它在較少的訓練下可以表現更好。主動學習者可以提出「查詢」，通常以未標記的數據實例的形式，由「神諭」（例如人類標註者）標記，他們已經了解問題的性質。這種方法在許多現代機器學習和數據挖掘應用中具有很好的動機，其中未標記的數據可能很豐富或容易獲得，但訓練標籤很難、耗時或昂貴。

本書是主動學習的一般介紹。它概述了幾種可能制定查詢的情景，並詳細介紹了許多查詢選擇算法，這些算法被組織成四個廣泛的類別，或稱為「查詢選擇框架」。我們還觸及了一些主動學習的理論基礎，並總結了這些方法在實踐中的優點和缺點，包括解決這些開放挑戰和機會的持續工作的概述。

目錄：自動化查詢 / 不確定性抽樣 / 在假設空間中搜索 / 最小化預期誤差和變異 / 利用數據中的結構 / 理論 / 實踐考慮事項