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Stochastic Gradient Descent and the Prediction of MeSH for PubMed Records
Stochastic Gradient Descent (SGD) has gained popularity for solving large scale supervised machine learning problems. It provides a rapid method for minimizing a number of loss functions and is applicable to Support Vector Machine (SVM) and Logistic optimizations. However SGD does not provide a conv...
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| 出版年: | AMIA Annu Symp Proc |
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| 主要な著者: | , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
American Medical Informatics Association
2014
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4419959/ https://ncbi.nlm.nih.gov/pubmed/25954431 |
| タグ: |
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