Symbolic Regression

Kronberger, Gabriel, Burlacu, Bogdan, Kommenda, Michael

Symbolic Regression
  • 出版商: CRC
  • 出版日期: 2024-08-16
  • 售價: $3,560
  • 貴賓價: 9.5 折 $3,382
  • 語言: 英文
  • 頁數: 308
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 113805481X
  • ISBN-13: 9781138054813

    • 海外代購書籍(需單獨結帳)

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商品描述

Symbolic regression (SR) is one of the most powerful machine learning techniques that produces transparent models, searching the space of mathematical expressions for a model that represents the relationship between the predictors and the dependent variable without the need of taking assumptions about the model structure. Currently, the most prevalent learning algorithms for SR are based on genetic programming (GP), an evolutionary algorithm inspired from the well-known principles of natural selection. This book is an in-depth guide to GP for SR, discussing its advanced techniques, as well as examples of applications in science and engineering.

The basic idea of GP is to evolve a population of solution candidates in an iterative, generational manner, by repeated application of selection, crossover, mutation, and replacement, thus allowing the model structure, coefficients, and input variables to be searched simultaneously. Given that explainability and interpretability are key elements for integrating humans into the loop of learning in AI, increasing the capacity for data scientists to understand internal algorithmic processes and their resultant models has beneficial implications for the learning process as a whole.

This book represents a practical guide for industry professionals and students across a range of disciplines, particularly data science, engineering, and applied mathematics. Focused on state-of-the-art SR methods and providing ready-to-use recipes, this book is especially appealing to those working with empirical or semi-analytical models in science and engineering.

商品描述(中文翻譯)

符號回歸（Symbolic regression, SR）是最強大的機器學習技術之一，能夠產生透明的模型，透過搜尋數學表達式的空間來尋找一個模型，以表示預測變數與依賴變數之間的關係，而無需對模型結構做出假設。目前，SR 最普遍的學習演算法是基於遺傳編程（Genetic Programming, GP），這是一種受到自然選擇原則啟發的進化演算法。本書是一本關於 GP 在 SR 中的深入指南，討論其先進技術以及在科學和工程中的應用範例。

GP 的基本理念是以迭代的世代方式進化一群解候選者，透過重複應用選擇、交叉、突變和替換，從而允許模型結構、係數和輸入變數同時被搜尋。考慮到可解釋性和可理解性是將人類納入 AI 學習循環的關鍵要素，增強數據科學家理解內部演算法過程及其產出模型的能力，對整體學習過程具有積極的影響。

本書是針對各種學科的業界專業人士和學生的實用指南，特別是數據科學、工程和應用數學。專注於最先進的 SR 方法並提供現成的實用配方，本書對於在科學和工程中使用經驗性或半解析模型的工作者特別具吸引力。

作者簡介

The authors are all affiliated with the University of Applied Sciences (UAS) Upper Austria.

Gabriel Kronberger is professor for data engineering and business intelligence. His research interests are symbolic regression and machine learning as well as probabilistic graphical models.

Bogdan Burlacu is a research assistant. His main focus is the study of genetic programming evolutionary dynamics in symbolic regression scenarios.

Michael Kommenda is a research assistant. He has been applying symbolic regression methods in various industrial projects and application scenarios.

Stephan M. Winkler is professor for medical and bioinformatics and head of the bioinformatics research group. His research interests despite bioinformatics include genetic programming, nonlinear model identification and machine learning.

Michael Affenzeller is professor for heuristic optimization and machine learning and head of the Heuristic and Evolutionary Algorithms Laboratory. Furthermore, he is the vice dean for research and overall head of the COMET project for heuristic optimization in production and logistics (HOPL).

作者簡介(中文翻譯)

作者皆隸屬於奧地利上部應用科技大學（UAS Upper Austria）。

**Gabriel Kronberger** 是數據工程與商業智慧的教授。他的研究興趣包括符號回歸、機器學習以及概率圖模型。

**Bogdan Burlacu** 是研究助理。他的主要研究重點是符號回歸情境中的遺傳編程進化動態。

**Michael Kommenda** 是研究助理。他在各種工業專案和應用情境中應用符號回歸方法。

**Stephan M. Winkler** 是醫學與生物資訊學的教授，並且是生物資訊研究小組的負責人。他的研究興趣除了生物資訊學外，還包括遺傳編程、非線性模型識別和機器學習。

**Michael Affenzeller** 是啟發式優化與機器學習的教授，並且是啟發式與進化演算法實驗室的負責人。此外，他還擔任研究副院長，並全面負責生產與物流中的啟發式優化COMET專案（HOPL）。

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