A semi-supervised machine learning framework for microRNA classification
نویسندگان
چکیده
منابع مشابه
A novel semi-supervised learning framework for hyperspectral image classification
In this paper, we propose a novel semi-supervised learning classification framework using box-based smooth ordering and Multiple 1D-embedding-based interpolation method in Ref. 25 for hyperspectral images. Due to the lack of labeled samples, conventional supervised approaches cannot generally perform efficient enough. On the other hand, obtaining labeled samples for hyperspectral image classifi...
متن کاملSemi-Supervised Learning for Neural Machine Translation
While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semisupervised approach for training NMT model...
متن کاملSemi-supervised learning for Machine Translation
Statistical machine translation systems are usually trained on large amounts of bilingual text which is used to learn a translation model, and also large amounts of monolingual text in the target language used to train a language model. In this chapter we explore the use of semi-supervised methods for the effective use of monolingual data from the source language in order to improve translation...
متن کاملSemi-Supervised Learning for Blog Classification
Blog classification (e.g., identifying bloggers’ gender or age) is one of the most interesting current problems in blog analysis. Although this problem is usually solved by applying supervised learning techniques, the large labeled dataset required for training is not always available. In contrast, unlabeled blogs can easily be collected from the web. Therefore, a semi-supervised learning metho...
متن کاملSemi-supervised learning for image classification
Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (< 10, 000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling proce...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Human Genomics
سال: 2019
ISSN: 1479-7364
DOI: 10.1186/s40246-019-0221-7