Feature Selection

Feature-Oriented Software Product Lines: Concepts and Implementation [Repost]  eBooks & eLearning

Posted by ChrisRedfield at April 2, 2015
Feature-Oriented Software Product Lines: Concepts and Implementation [Repost]

Sven Apel, Don Batory, Christian Kästner, Gunter Saake - Feature-Oriented Software Product Lines: Concepts and Implementation
Published: 2013-10-09 | ISBN: 3642375200 | PDF | 315 pages | 8 MB

Feature-Oriented Software Product Lines: Concepts and Implementation  eBooks & eLearning

Posted by libr at Oct. 7, 2013
Feature-Oriented Software Product Lines: Concepts and Implementation

Feature-Oriented Software Product Lines: Concepts and Implementation by Sven Apel, Don Batory, Christian Kästner and Gunter Saake
English | 2013 | ISBN: 3642375200 | 330 pages | PDF | 8,8 MB

While standardization has empowered the software industry to substantially scale software development and to provide affordable software to a broad market, it often does not address smaller market segments, nor the needs and wishes of individual customers.

Feature-Oriented Software Product Lines: Concepts and Implementation  eBooks & eLearning

Posted by hill0 at April 4, 2017
Feature-Oriented Software Product Lines: Concepts and Implementation

Feature-Oriented Software Product Lines: Concepts and Implementation by Sven Apel
English | 9 Oct. 2013 | ISBN: 3642375200 | 332 Pages | EPUB (True) | 4.49 MB

While standardization has empowered the software industry to substantially scale software development and to provide affordable software to a broad market, it often does not address smaller market segments,

The Art of Feature Engineering: Essentials for Machine Learning  eBooks & eLearning

Posted by hill0 at May 30, 2020
The Art of Feature Engineering: Essentials for Machine Learning

The Art of Feature Engineering: Essentials for Machine Learning
by Pablo Duboue

English | 2020 | ISBN: 1108709389 | 287 Pages | PDF | 8 MB
A Feature-Centric View of Information Retrieval (The Information Retrieval Series)

Donald Metzler, "A Feature-Centric View of Information Retrieval (The Information Retrieval Series)"
Publisher: Springer | ISBN 10: 3642228976 | 2011 | PDF | 177 pages | 2.4 MB

Python 3 and Feature Engineering  eBooks & eLearning

Posted by ELK1nG at Dec. 15, 2023
Python 3 and Feature Engineering


Python 3 and Feature Engineering
English | 2023 | ISBN: 1683929470 | 273 pages | True EPUB | 4.67 MB

Python 3 and Feature Engineering  eBooks & eLearning

Posted by hill0 at May 9, 2024
Python 3 and Feature Engineering

Python 3 and Feature Engineering
English | 2024 | ISBN: 1683929497 | 216 Pages | EPUB (True) | 2.4 MB

Computational Intelligence in Multi-Feature Visual Pattern Recognition  eBooks & eLearning

Posted by ChrisRedfield at Sept. 24, 2014
Computational Intelligence in Multi-Feature Visual Pattern Recognition

Pramod Pisharady, Prahlad Vadakkepat, Loh Ai Poh - Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition using Biologically Inspired Approaches
Published: 2014-06-25 | ISBN: 9812870555 | PDF | 138 pages | 5 MB

Computational Intelligence in Multi-Feature Visual Pattern Recognition (Repost)  eBooks & eLearning

Posted by DZ123 at June 12, 2018
Computational Intelligence in Multi-Feature Visual Pattern Recognition (Repost)

Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh, "Computational Intelligence in Multi-Feature Visual Pattern Recognition"
English | 2014 | ISBN: 9812870555 | PDF | pages: 142 | 5.7 mb
Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach

Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach - Y-h. Taguchi
English | 2024 | 555 Pages | ISBN: 3031609816 | PDF EPUB (True) | 102.91 MB

This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology.