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Record Nr. |
UNINA9910954803903321 |
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Titolo |
Dataset shift in machine learning / / [edited by] Joaquin Quinonero-Candela ... [et al.] |
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Pubbl/distr/stampa |
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Cambridge, Mass., : MIT Press, c2009 |
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ISBN |
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9780262292535 |
026229253X |
9781282240384 |
1282240382 |
9780262255103 |
0262255103 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (246 p.) |
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Collana |
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Neural information processing series |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Machine learning |
Machine learning - Mathematical models |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Contents; Series Foreword; Preface; I - Introduction to Dataset Shift; 1 - When Training and Test Sets Are Different: Characterizing Learning Transfer; 2 - Projection and Projectability; II - Theoretical Views on Dataset and Covariate Shift; 3 - Binary Classi cation under Sample Selection Bias; 4 - On Bayesian Transduction: Implications for the Covariate Shift Problem; 5 - On the Training/Test Distributions Gap: A Data Representation Learning Framework; III - Algorithms for Covariate Shift; 6 - Geometry of Covariate Shift with Applications to Active Learning |
7 - A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift 8 - Covariate Shift by Kernel Mean Matching; 9 - Discriminative Learning under Covariate Shift with a Single Optimization Problem; 10 - An Adversarial View of Covariate Shift and a Minimax Approach; IV - Discussion; 11 - Author Comments; References; Notation and Symbols; Contributors; Index |
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Sommario/riassunto |
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This work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when |
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