| 000 | 02508nam a22003375i 4500 | ||
|---|---|---|---|
| 001 | 102721 | ||
| 003 | DE-He213 | ||
| 005 | 20230102113056.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 170928s2018 gw | s |||| 0|eng d | ||
| 020 | _a9783319663081 | ||
| 024 | 7 |
_a10.1007/978-3-319-66308-1 _2doi |
|
| 050 | 4 |
_aQ325.5 _bH664 2018 EB |
|
| 100 | 1 |
_aHoogendoorn, Mark _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/nb2013013241 _1http://viaf.org/viaf/291958681/ |
|
| 245 | 1 | 0 |
_aMachine Learning for the Quantified Self _bOn the Art of Learning from Sensory Data _cby Mark Hoogendoorn, Burkhardt Funk. |
| 264 | 1 |
_aCham _bSpringer International Publishing _c2018 |
|
| 300 | _a1 recurso en línea (XV, 231 páginas 89 ilustraciones, 72 ilustraciones a color) | ||
| 347 |
_atext file _bPDF |
||
| 490 | 0 |
_aCognitive Systems Monographs _x1867-4925 _v35 |
|
| 520 | 3 | _aThis 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 | 7 | _Inteligencia artificial | |
| 650 | 7 |
_aRobótica _2embne _9160676 |
|
| 700 | 1 |
_aFunk, Burkhardt _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/308722351/ |
|
| 776 | 0 | 8 |
_iEdición impresa: _z9783319663074 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319663098 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319882154 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-66308-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 490 | 0 | _aEngineering (Springer-11647) | |
| 988 | _aEBSPRINGER_2018 | ||
| 998 |
_b01/2019 _dz _ep _feng _ggw _h0 |
||
| 999 |
_c102721 _d102721 _x1 |
||