| 000 | 01376nam a2200289 a 4500 | ||
|---|---|---|---|
| 001 | vtls000070630 | ||
| 003 | KUKTEM | ||
| 005 | 20251125095427.0 | ||
| 008 | 130605t2012 enka f 001 0 eng d | ||
| 020 | _a9781107096394 (hbk.) | ||
| 020 | _a1107096391 (hbk.) | ||
| 020 | _a9781107422223 (pbk.) | ||
| 020 | _a1107422221 (pbk.) | ||
| 039 | 9 |
_a201401101026 _bsaini _y201306051114 _zhairil |
|
| 040 | _aUMP | ||
| 090 | _aQ325.5 .F53 2012 | ||
| 100 | 1 | _aFlach, Peter A. | |
| 245 | 1 | 0 |
_aMachine learning : _bthe art and science of algorithms that make sense of data / _cPeter Flach |
| 260 |
_aCambridge : _bCambridge University Press, _c2012 |
||
| 300 |
_axvii, 396 p. : _bcol. ill. ; _c25 cm. |
||
| 504 | _aIncludes bibliographical references and index | ||
| 505 | 0 | _a1. The ingredients of machine learning -- 2. Binary classification and related tasks -- 3. Beyond binary classification -- 4. Concept learning -- 5. Tree models -- 6. Rule models -- 7. Linear models -- 8. Distance-based models -- 9. Probabilistic models -- 10. Features -- 11. Model ensembles -- 12. Machine learning experiments -- Epilogue: where to go from here | |
| 650 | 0 |
_aMachine learning _vTextbooks |
|
| 650 | 7 |
_aApprentissage automatique _xManuels scolaires. _2ram |
|
| 999 |
_aVIRTUA40 _c71248 _d71254 |
||
| 999 | _aVTLSSORT0080*0200*0201*0202*0203*0400*0900*1000*2450*2600*3000*5040*5050*6500*6501*9992 | ||
| 942 | 0 | 0 | _03 |