Literature DB >> 26185243

Machine learning: Trends, perspectives, and prospects.

M I Jordan1, T M Mitchell2.   

Abstract

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
Copyright © 2015, American Association for the Advancement of Science.

Entities:  

Mesh:

Year:  2015        PMID: 26185243     DOI: 10.1126/science.aaa8415

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  368 in total

Review 1.  Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants.

Authors:  Molly Megraw; Jason S Cumbie; Maria G Ivanchenko; Sergei A Filichkin
Journal:  Plant Cell       Date:  2016-02-11       Impact factor: 11.277

Review 2.  Precision medicine in cardiology.

Authors:  Elliott M Antman; Joseph Loscalzo
Journal:  Nat Rev Cardiol       Date:  2016-06-30       Impact factor: 32.419

3.  Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy.

Authors:  Ling Zhang; Andreas Wahle; Zhi Chen; John J Lopez; Tomas Kovarnik; Milan Sonka
Journal:  IEEE Trans Med Imaging       Date:  2017-07-11       Impact factor: 10.048

4.  Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

Authors:  Paul T Ogink; Aditya V Karhade; Quirina C B S Thio; Stuart H Hershman; Thomas D Cha; Christopher M Bono; Joseph H Schwab
Journal:  Eur Spine J       Date:  2019-03-27       Impact factor: 3.134

5.  Recommending teams promotes prosocial lending in online microfinance.

Authors:  Wei Ai; Roy Chen; Yan Chen; Qiaozhu Mei; Webb Phillips
Journal:  Proc Natl Acad Sci U S A       Date:  2016-12-14       Impact factor: 11.205

6.  Science and data science.

Authors:  David M Blei; Padhraic Smyth
Journal:  Proc Natl Acad Sci U S A       Date:  2017-08-07       Impact factor: 11.205

Review 7.  Deep learning for healthcare: review, opportunities and challenges.

Authors:  Riccardo Miotto; Fei Wang; Shuang Wang; Xiaoqian Jiang; Joel T Dudley
Journal:  Brief Bioinform       Date:  2018-11-27       Impact factor: 11.622

Review 8.  Predictive analytics in mental health: applications, guidelines, challenges and perspectives.

Authors:  T Hahn; A A Nierenberg; S Whitfield-Gabrieli
Journal:  Mol Psychiatry       Date:  2016-11-15       Impact factor: 15.992

9.  Neuroimaging Research: From Null-Hypothesis Falsification to Out-of-Sample Generalization.

Authors:  Danilo Bzdok; Gaël Varoquaux; Bertrand Thirion
Journal:  Educ Psychol Meas       Date:  2016-10-06       Impact factor: 2.821

10.  Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning.

Authors:  Morgan Heisler; Myeong Jin Ju; Mahadev Bhalla; Nathan Schuck; Arman Athwal; Eduardo V Navajas; Mirza Faisal Beg; Marinko V Sarunic
Journal:  Biomed Opt Express       Date:  2018-10-10       Impact factor: 3.732

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