An Elementary Introduction to Statistical Learning Theory (Hardcover)

Sanjeev Kulkarni, Gilbert Harman

買這商品的人也買了...

商品描述

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning

A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference.

Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting.

Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study.

An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

商品描述(中文翻譯)

《統計學習理論初級入門》是一本引人深思的書籍,探討統計學習理論在理解人類學習和歸納推理中的角色。這是一個由哲學和電機工程領域的領先研究人員共同努力的成果,是一本全面且易於理解的入門書,介紹了統計模式識別和統計學習理論這兩個快速發展的領域。作者以一種在其他相關書籍中很少見的方式和水平解釋了這些領域,並將當代機器學習的基本理論作為哲學思考歸納推理的框架。

本書提倡統計學習的基本目標,即了解可實現和不可實現的內容,並展示了系統方法與評估學習系統性能所需技術相結合的價值。首先,介紹了機器學習,並簡要討論了圖像識別、語音識別、醫學診斷和統計套利等應用。為了提高易讀性,書中還提供了兩章有關概率理論的相關內容。隨後的章節涵蓋了模式識別問題、最優貝葉斯決策規則、最近鄰規則、核規則、神經網絡、支持向量機和提升等主題。

書中的附錄探討了所討論材料與數學、哲學、心理學和統計學等相關主題之間的關係,並在這些領域的問題與統計學習理論之間建立了深入的聯繫。每章結束時都有一個摘要部分、一組練習問題和一個參考部分,提供歷史註解和進一步學習資源。

《統計學習理論初級入門》是高年級本科生和研究生統計學習理論、模式識別和機器學習課程的優秀教材。它也是工程、計算機科學、哲學和認知科學領域的研究人員和從業人員的入門參考書,可以進一步提升他們對這一主題的知識水平。