Machine Learning for the Quantified Self On the Art of Learning from Sensory Data /
This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from state-of-the-art sci...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham :
Springer International Publishing,
2018.
|
| Ausgabe: | 1st ed. 2018. |
| Schriftenreihe: | Cognitive Systems Monographs,
35 |
| Schlagworte: | |
| ISBN: | 9783319663081 |
| ISSN: | 1867-4925 ; |
| Online-Zugang: |
|
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 003 | SK-BrCVT | ||
| 005 | 20220618101253.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 170928s2018 gw | s |||| 0|eng d | ||
| 020 | |a 9783319663081 | ||
| 024 | 7 | |a 10.1007/978-3-319-66308-1 |2 doi | |
| 035 | |a CVTIDW11294 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hoogendoorn, Mark. |4 aut | |
| 245 | 1 | 0 | |a Machine Learning for the Quantified Self |h [electronic resource] : |b On the Art of Learning from Sensory Data / |c by Mark Hoogendoorn, Burkhardt Funk. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Cham : |b Springer International Publishing, |c 2018. | |
| 300 | |a XV, 231 p. 89 illus., 72 illus. in color. |b online resource. | ||
| 490 | 1 | |a Cognitive Systems Monographs, |x 1867-4925 ; |v 35 | |
| 500 | |a Engineering | ||
| 516 | |a text file PDF | ||
| 520 | |a This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are sample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Artificial intelligence. | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-3-319-66308-1 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE08574 | ||
| 919 | |a 978-3-319-66308-1 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
| 999 | |c 236999 |d 236999 | ||