Statistical Analysis of Complex Data

Mario Fordellone

ISBN 10: 6200443726 / ISBN 13: 9786200443724

Published by LAP LAMBERT Academic Publishing Okt 2019, 2019

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Statistical learning (SL) is the study of the generalizable extraction of knowledge from data (Friedman et al. 2001). The concept of learning is used when human expertise does not exist, humans are unable to explain their expertise, solution changes in time, solution needs to be adapted to particular cases. The principal algorithms used in SL are classified in: supervised learning (e.g. regression and classification), unsupervised learning (e.g. association and clustering), semi-supervised, it combines both labeled and unlabeled examples to generate an appropriate function or classifier. Following this research idea, in this book we propose a good review on the more recent statistical models used to solve the dimensionality problem recently discussed.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch.

Seller Inventory # 9786200443724

Bibliographic Details

• Title: Statistical Analysis of Complex Data
• Publisher: LAP LAMBERT Academic Publishing Okt 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 6200443726
• ISBN 13: 9786200443724
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 143 grams

About this title

Synopsis

Statistical learning (SL) is the study of the generalizable extraction of knowledge from data (Friedman et al. 2001). The concept of learning is used when human expertise does not exist, humans are unable to explain their expertise, solution changes in time, solution needs to be adapted to particular cases. The principal algorithms used in SL are classified in: supervised learning (e.g. regression and classification), unsupervised learning (e.g. association and clustering), semi-supervised, it combines both labeled and unlabeled examples to generate an appropriate function or classifier. Following this research idea, in this book we propose a good review on the more recent statistical models used to solve the dimensionality problem recently discussed.