LEADER 02971nam 22005295 450 001 9910299895503321 005 20200701044629.0 010 $a3-319-66308-9 024 7 $a10.1007/978-3-319-66308-1 035 $a(CKB)4100000000586806 035 $a(DE-He213)978-3-319-66308-1 035 $a(MiAaPQ)EBC5061636 035 $a(PPN)204534674 035 $a(EXLCZ)994100000000586806 100 $a20170928d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for the Quantified Self $eOn the Art of Learning from Sensory Data /$fby Mark Hoogendoorn, Burkhardt Funk 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XV, 231 p. 89 illus., 72 illus. in color.) 225 1 $aCognitive Systems Monographs,$x1867-4925 ;$v35 311 $a3-319-66307-0 320 $aIncludes bibliographical references at the end of each chapters and index. 330 $aThis book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are sample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users. 410 0$aCognitive Systems Monographs,$x1867-4925 ;$v35 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.31 700 $aHoogendoorn$b Mark$4aut$4http://id.loc.gov/vocabulary/relators/aut$01065053 702 $aFunk$b Burkhardt$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299895503321 996 $aMachine Learning for the Quantified Self$92542807 997 $aUNINA