Name: Small Sample Modelling Based on Deep and Broad Forest Regression. Theory and Industrial Application
SKU: 64763733
Price: 150.64 USD
Availability: InStock
Author: Jian Tang;Wen , Mexico City, Mexico) Yu;Junfei Qiao
ISBN: 9780443315640

portada Small Sample Modelling Based on Deep and Broad Forest Regression. Theory and Industrial Application

Small Sample Modelling Based on Deep and Broad Forest Regression. Theory and Industrial Application

Formato

Libro Físico

Editorial

Academic Press Inc

Autor

Jian Tang;Wen , Mexico City, Mexico) Yu;Junfei Qiao

Colección

Emerging Methodologies and Applications in Modelling, Identification and Control

Año

2025

N° páginas

250

Encuadernación

Tapa Blanda

Dimensiones

9.02x5.98 in

ISBN13

9780443315640

Small Sample Modelling Based on Deep and Broad Forest Regression. Theory and Industrial Application

Jian Tang;Wen , Mexico City, Mexico) Yu;Junfei Qiao (Autor) · Academic Press Inc · Tapa Blanda

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Reseña del libro "Small Sample Modelling Based on Deep and Broad Forest Regression. Theory and Industrial Application"

Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encompassing classical ensemble learning, tree-structured deep forest classification, and broad learning systems with neural networks. It introduces an innovative deep/broad learning algorithm for small-sample industrial modeling tasks. The book is divided into two parts: methodology and practical application in dioxin emission modeling. Methodology sections include Preliminaries, Deep Forest Regression, Broad Forest Regression, and Fuzzy Forest Regression. The application part focuses on modeling dioxin emissions in municipal solid waste incineration. Throughout, various tree-structured strategies are presented, and the authors provide software systems for validating these methods. This book is suitable for advanced undergraduates, graduate engineering students, and practicing engineers looking for self-study resources.