Check order status

Become part of a community of book lovers from all over the world and get access to a whole bunch of benefits. Create an account for free

DPD courier 4.99 € GLS courier 11.49 €

Contact

How to shop

Help

My account

▸ Empty :-(

Evolutionary Machine Learning in Linguistic Knowledge Extraction

Name: Evolutionary Machine Learning in Linguistic Knowledge Extraction
Brand: LAP Lambert Academic Publishing
SKU: 13411736
Price: 55.02 EUR
Availability: InStock
Author: Lamiaa Ahmed
ISBN: 9783659891038

Lamiaa Ahmed

Amr Badr

Mostafa Abd El-Azim

Language

English

Book Paperback

Libristo code: 13411736

Publishers LAP Lambert Academic Publishing, November 2015

This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - M... Full description

Libristo code: 13411736

133 b

55.02 €

In stock at our supplier Shipping in 5-8 days

30-day return policy

Customers also purchased

Bulletin Société des antiquaires de l'Ouest

Paperback

62.19 €

Watercolor Wow Anna Malek

Paperback

16.95 €

Aspectos básicos de la gestión de recursos humanos

Paperback

75.41 €

Kevin Mallea Esce Dalla Sua Camera Marco Fornaciari

Paperback

12.51 €

Jabra Ibrahim Jabra: Faces of the Romantic Intellectual (Jabra Ibrahim Jabra: Wojouh Al-Mothaqqaf Al- Romancy) Faisal Darraj

Paperback

11.00 €

Leyes de educacion Andrés Santana Muñoz

Paperback

52.49 €

This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - Michigan and Pittsburgh - for designing fuzzy rule-based classification systems. The search ability of each approach is examined to efficiently find fuzzy rule-based systems with high classification accuracy. These two approaches are combined into a single hybrid algorithm. The generalization ability of fuzzy rule-based classification systems, designed by the proposed hybrid algorithm is examined on real data sets. Experimental results show that the hybrid algorithm has higher search ability within a population of individual rules and within a population of rule sets.