| 000 | 01778nam a2200277 a 4500 | ||
|---|---|---|---|
| 001 | vtls000070612 | ||
| 003 | KUKTEM | ||
| 005 | 20251125093717.0 | ||
| 008 | 130605t2012 enka f 001 0 eng d | ||
| 020 | _a9780521190176 (hardback) | ||
| 020 | _a0521190177 (hardback) | ||
| 039 | 9 |
_a201401061450 _bsaini _y201306051100 _zhairil |
|
| 040 | _aUMP | ||
| 090 | _aQA276.8 .S84 2012 | ||
| 100 | 1 |
_aSugiyama, Masashi, _d1974- |
|
| 245 | 1 | 0 |
_aDensity ratio estimation in machine learning / _cMasashi Sugiyama, Taiji Suzuki, Takafumi Kanamori |
| 260 |
_aCambridge : _bCambridge University Press, _c2012 |
||
| 300 |
_axii, 329 p. : _bill. ; _c24 cm. |
||
| 504 | _aIncludes bibliographical references and index | ||
| 505 | 0 | _aPart I. Density-Ratio Approach to Machine Learning: 1. Introduction -- Part II. Methods of Density-Ratio Estimation: 2. Density estimation; 3. Moment matching; 4. Probabilistic classification; 5. Density fitting; 6. Density-ratio fitting; 7. Unified framework; 8. Direct density-ratio estimation with dimensionality reduction -- Part III. Applications of Density Ratios in Machine Learning: 9. Importance sampling; 10. Distribution comparison; 11. Mutual information estimation; 12. Conditional probability estimation -- Part IV. Theoretical Analysis of Density-Ratio Estimation: 13. Parametric convergence analysis; 14. Non-parametric convergence analysis; 15. Parametric two-sample test; 16. Non-parametric numerical stability analysis -- Part V. Conclusions: 17. Conclusions and future directions | |
| 650 | 0 | _aEstimation theory | |
| 650 | 0 | _aMachine learning | |
| 700 | 1 |
_aSuzuki, Taiji, _d1981- |
|
| 700 | 1 |
_aKanamori, Takafumi, _d1971- |
|
| 999 |
_aVIRTUA40 _c67396 _d67402 |
||
| 999 | _aVTLSSORT0080*0200*0201*0400*0900*1000*2450*2600*3000*5040*5050*6500*6501*7000*7001*9992 | ||