Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	Januar 2023
Genre:	EDV / Informatik
	ArtificialIntelligence / autoencoder / datamining / Datenbanken / Datenmanagement / DimensionalityReduction / localitysensitivehashing / machinelearning / principalcomponent / Representation mehr...
ISBN:	9789811979071
EAN-Code:	9789811979071
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 7 mm
Gewicht:	172 gr
Seiten:	104
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques¿ effectiveness.