Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation (Hardcover)

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

隨著計算能力在過去幾十年的快速增長，機器學習這個領域在理論和實踐上都有了快速的發展。機器學習方法通常基於一個假設，即數據生成機制在時間上不會改變。然而，機器學習在現實世界的應用中，包括圖像識別、自然語言處理、語音識別、機器人控制和生物信息學等，往往違反了這個常見的假設。處理非穩態性是現代機器學習面臨的最大挑戰之一。本書專注於一種特定的非穩態環境，稱為協變量漂移，其中輸入（查詢）的分佈發生變化，但輸出（答案）的條件分佈保持不變，並介紹了機器學習理論、算法和應用來克服這種非穩態性。在回顧該領域的最新研究後，作者們討論了包括在協變量漂移下學習、模型選擇、重要性估計和主動學習等主題。他們描述了協變量漂移適應的實際應用，如腦-電腦界面、語者識別和從面部圖像預測年齡。通過這本書，他們旨在鼓勵未來在機器學習、統計和工程領域的研究，致力於創造能夠在非穩態環境下學習的真正自主學習機器。