Sequence–structure relationship study in all-α transmembrane proteins using an unsupervised learning approach

Amino Acids ◽  
2015 ◽  
Vol 47 (11) ◽  
pp. 2303-2322 ◽  
Author(s):  
Jérémy Esque ◽  
Aurélie Urbain ◽  
Catherine Etchebest ◽  
Alexandre G. de Brevern
Keyword(s):  
Unsupervised Learning ◽  
Transmembrane Proteins ◽  
Learning Approach ◽  
Sequence Structure ◽  
Structure Relationship ◽  
Relationship Study

A Novel Unsupervised Learning Approach for Assessing Web Services Refactoring

2019 ◽  
pp. 273-284
Author(s):  
Guillermo Rodriguez ◽  
Cristian Mateos ◽  
Luciano Listorti ◽  
Brian Hammer ◽  
Sanjay Misra
Keyword(s):  
Web Services ◽  
Unsupervised Learning ◽  
Learning Approach

A Search Engine Development Utilizing Unsupervised Learning Approach

2015 ◽  
pp. 223-233
Author(s):  
Mohd Noah Abdul Rahman ◽  
Afzaal H. Seyal ◽  
Mohd Saiful Omar ◽  
Siti Aminah Maidin
Keyword(s):  
Unsupervised Learning ◽  
Search Engine ◽  
Learning Approach ◽  
Engine Development

Semisupervised sentiment analysis method for online text reviews

2020 ◽  
pp. 016555152091003
Author(s):  
Gyeong Taek Lee ◽  
Chang Ouk Kim ◽  
Min Song
Keyword(s):  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Model Space ◽  
Training Dataset ◽  
Learning Approach ◽  
Learning Models ◽  
Text Data ◽  
Learning Techniques ◽  
Sentiment Dictionary

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

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