Name: Symbolic Regression (en Inglés)
Price: 105590.00 CLP
Availability: InStock
Author: Kronberger, Gabriel ; Burlacu, Bogdan ; Kommenda, Michael
ISBN: 9781138054813

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

298

Encuadernación

Tapa Dura

ISBN13

9781138054813

N° edición

Categorías

Desarrollo y programación de juegos
Data mining: minería de datos
Teoría matemática de la computación
Machine learning: aprendizaje automático

Symbolic Regression (en Inglés)

Gabriel Kronberger (Autor) · Bogdan Burlacu (Autor) · Michael Kommenda (Autor) · CRC Press · Tapa Dura

Libro Nuevo

$ 105.590

$ 175.980

Ahorras: $ 70.390

40% descuento

Envío normal

Origen: Reino Unido (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Viernes 14 de Junio y el Miércoles 26 de Junio.

Lo recibirás en cualquier lugar de Chile entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Symbolic Regression (en Inglés)"

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. Focussed 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.