Algorithmic Learning in a Random World
暫譯: 隨機世界中的演算法學習

Vovk, Vladimir, Gammerman, Alexander, Shafer, Glenn

Algorithmic Learning in a Random World
  • 出版商: Springer
  • 出版日期: 2023-12-14
  • 售價: $7,100
  • 貴賓價: 9.5 折 $6,745
  • 語言: 英文
  • 頁數: 476
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031066510
  • ISBN-13: 9783031066511
  • 相關分類: Algorithms-data-structures

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

商品描述(中文翻譯)

《隨機世界中的算法學習》描述了在構建可計算的科爾莫哥洛夫（Kolmogorov）算法隨機性概念的近似值方面的最新理論和實驗發展。基於這些近似值，開發了一組新的機器學習算法，這些算法可以用來進行預測，並在高維空間中估計其信心和可信度，前提是數據獨立且同分佈（隨機性假設）。這本獨特的專著的另一個目的是概述預測的一些限制：基於算法隨機性理論的方法允許在某些情況下證明預測的不可能性。書中描述了幾個重要的機器學習問題，例如在高維空間中的密度估計，如果唯一的假設是隨機性，則無法解決。

作者簡介

Vladimir Vovk is Professor of Computer Science at Royal Holloway, University of London. His research interests include machine learning and the foundations of probability and statistics. He was one of the founders of prediction with expert advice, an area of machine learning avoiding making any statistical assumptions about the data. Together with Glenn Shafer and with original inspiration from Philip Dawid, he developed game-theoretic foundations for probability and statistics.

Alexander Gammerman is Professor of Computer Science and co-Director of the Centre for Reliable Machine Learning at Royal Holloway, University of London. His research interests lie in machine learning and pattern recognition, where the majority of his research books, papers, and grants can be found. He is a Fellow of the Royal Statistical Society and has held visiting and honorary professorships from several universities in Europe and the USA.

Glenn Shafer is Professor and former Dean of the Rutgers Business School - Newark and New Brunswick. He is best known for his work in the 1970s and 1980s on the Dempster-Shafer theory, an alternative theory of probability that has been applied widely in engineering and artificial intelligence. Glenn is also known for his initiation, with Vladimir Vovk, of the game-theoretic framework for probability. Their first book on the topic was Probability and Finance: It's Only a Game! A new book on the topic, Game-Theoretic Foundations for Probability and Finance, published in 2019 (Wiley).

作者簡介(中文翻譯)

弗拉基米爾·沃夫克（Vladimir Vovk）是倫敦大學皇家霍洛威學院的計算機科學教授。他的研究興趣包括機器學習以及概率和統計的基礎。他是專家建議預測（prediction with expert advice）的創始人之一，這是一個在機器學習中避免對數據做出任何統計假設的領域。與格倫·謝弗（Glenn Shafer）合作，並受到菲利普·道威德（Philip Dawid）的原始啟發，他發展了概率和統計的博弈論基礎。

亞歷山大·甘默曼（Alexander Gammerman）是倫敦大學皇家霍洛威學院的計算機科學教授及可靠機器學習中心的共同主任。他的研究興趣集中在機器學習和模式識別上，他的大多數研究書籍、論文和資助均可在此領域找到。他是英國皇家統計學會的會員，並曾在歐洲和美國的多所大學擔任訪問和榮譽教授。

格倫·謝弗（Glenn Shafer）是羅格斯商學院（Rutgers Business School） - 紐瓦克和新布倫瑞克的教授及前院長。他因在1970年代和1980年代對邊斯特-謝弗理論（Dempster-Shafer theory）的研究而聞名，這是一種替代的概率理論，已廣泛應用於工程和人工智慧領域。格倫還因與弗拉基米爾·沃夫克共同創立概率的博弈論框架而聞名。他們在該主題上的第一本書是Probability and Finance: It's Only a Game!，而在2019年出版的新書Game-Theoretic Foundations for Probability and Finance（Wiley）。

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