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Internet use and psychological wellbeing among older adults in England: a difference-in-differences analysis over the COVID-19 pandemic
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- Journal:
- Psychological Medicine / Volume 53 / Issue 11 / August 2023
- Published online by Cambridge University Press:
- 30 September 2022, pp. 5356-5358
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EPA-0606 – Relationship Between Serotonin Transporter and Bone Loss
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- European Psychiatry / Volume 29 / Issue S1 / 2014
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- 15 April 2020, p. 1
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P-1094 - Treatment of Nightmares With Prazosin: a Systematic Review
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- European Psychiatry / Volume 27 / Issue S1 / 2012
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- 15 April 2020, p. 1
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MP03: Strategies to minimize impact of electronic health record implementation on emergency department flow
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- Canadian Journal of Emergency Medicine / Volume 21 / Issue S1 / May 2019
- Published online by Cambridge University Press:
- 02 May 2019, pp. S42-S43
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- May 2019
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Cross-national comparisons of increasing suicidal mortality rates for Koreans in the Republic of Korea and Korean Americans in the USA, 2003–2012
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- Epidemiology and Psychiatric Sciences / Volume 27 / Issue 1 / February 2018
- Published online by Cambridge University Press:
- 10 November 2016, pp. 62-73
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Preliminary report on the engineering development of the Magnus Aerospace Corp. LTA 20-1 heavy-lift aircraft
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- The Aeronautical Journal / Volume 87 / Issue 864 / April 1983
- Published online by Cambridge University Press:
- 04 July 2016, pp. 119-131
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11 - Support vector learning models for outlier detection
- from Part V - Support vector machines and variants
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 380-394
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15 - Kernel methods for estimation, prediction, and system identification
- from Part VII - Kernel methods and statistical estimation theory
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 494-536
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5 - Unsupervised learning for cluster discovery
- from Part III - Unsupervised learning models for cluster analysis
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 141-177
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7 - Kernel-based regression and regularization analysis
- from Part IV - Kernel ridge regressors and variants
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 221-247
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9 - Kernel ridge regression for super vised classification
- from Part IV - Kernel ridge regressors and variants
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 282-340
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1 - Fundamentals of kernel-based machine learning
- from Part I - Machine learning and kernel vector spaces
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 3-43
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Part VI - Kernel methods for green machine learning technologies
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 419-420
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Part III - Unsupervised learning models for cluster analysis
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 139-140
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Appendix A - Validation and testing of learning models
- from Part VIII - Appendices
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 539-548
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12 - Ridge-SVM learning models
- from Part V - Support vector machines and variants
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 395-418
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13 - Efficient kernel methods for learning and classification
- from Part VI - Kernel methods for green machine learning technologies
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 421-456
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Part V - Support vector machines and variants
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 341-342
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Frontmatter
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp i-iv
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8 - Linear regression and discriminant analysis for supervised classification
- from Part IV - Kernel ridge regressors and variants
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- Kernel Methods and Machine Learning
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- 05 July 2014
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- 17 April 2014, pp 248-281
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