Symbolic Regression
暫譯: 符號回歸

Kronberger, Gabriel, Burlacu, Bogdan, Kommenda, Michael

Symbolic Regression
  • 出版商: CRC
  • 出版日期: 2026-07-20
  • 售價: $2,570
  • 貴賓價: 9.5 折 $2,441
  • 語言: 英文
  • 頁數: 308
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1032787058
  • ISBN-13: 9781032787053
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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.

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，它能產生透明的模型，透過搜尋數學表達式的空間來尋找一個模型，該模型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。

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奧地利應用科技大學（UAS）。

Gabriel Kronberger 是資料工程與商業智慧的教授。他的研究興趣包括符號迴歸、機器學習以及機率圖模型。

Bogdan Burlacu 是研究助理。他的主要研究重點是符號迴歸情境中的遺傳程式設計進化動態。

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迴歸方法。

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，還包括遺傳程式設計、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，他是研究副院長，並且是生產與物流中啟發式優化的COMET專案（HOPL）的總負責人。

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