Matches in Library of Congress for { <http://lccn.loc.gov/2012289353> ?p ?o. }
Showing items 1 to 25 of
25
with 100 items per page.
- 2012289353 abstract "'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.".
- 2012289353 contributor B12489775.
- 2012289353 created "2012.".
- 2012289353 date "2012".
- 2012289353 date "2012.".
- 2012289353 dateCopyrighted "2012.".
- 2012289353 description "'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.".
- 2012289353 description "1. 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.".
- 2012289353 description "Includes bibliographical references (p. 367-381) and index.".
- 2012289353 extent "xvii, 396 p. :".
- 2012289353 identifier "1107096391 (hbk.)".
- 2012289353 identifier "1107422221 (pbk.)".
- 2012289353 identifier "9781107096394 (hbk.)".
- 2012289353 identifier "9781107422223 (pbk.)".
- 2012289353 issued "2012".
- 2012289353 issued "2012.".
- 2012289353 language "eng".
- 2012289353 publisher "Cambridge ; New York : Cambridge University Press,".
- 2012289353 subject "006.31 23".
- 2012289353 subject "Apprentissage automatique Manuels scolaires. ram".
- 2012289353 subject "Machine learning Textbooks.".
- 2012289353 subject "Q325.5 .F53 2012".
- 2012289353 tableOfContents "1. 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.".
- 2012289353 title "Machine learning : the art and science of algorithms that make sense of data / Peter Flach.".
- 2012289353 type "text".