A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classification of English and Thai Text

2019 ◽  
pp. 81-93 ◽  
Author(s):  
Rangsipan Marukatat
Keyword(s):  
Comparative Study ◽  
Word Embedding ◽  
Bag Of Words

scholarly journals CNN architecture comparison for radio galaxy classification

2021 ◽  
Vol 503 (2) ◽  
pp. 1828-1846
Author(s):  
Burger Becker ◽  
Mattia Vaccari ◽  
Matthew Prescott ◽  
Trienko Grobler
Keyword(s):  
Comparative Study ◽  
Galaxy Evolution ◽  
Radio Galaxy ◽  
Recognition Performance ◽  
Model Complexity ◽  
Great Promise ◽  
Radio Sources ◽  
Morphological Classification ◽  
Computational Performance

ABSTRACT The morphological classification of radio sources is important to gain a full understanding of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citizen scientists, and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is convolutional neural networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. First, a proper analysis of whether overfitting occurs when training CNNs to perform radio galaxy morphological classification using a small curated training set is needed. Secondly, a good comparative study regarding the practical applicability of the CNN architectures in literature is required. Both of these shortcomings are addressed in this paper. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, computational complexity, and mean per class accuracy. As part of this study, we also investigate the effect that receptive field, stride length, and coverage have on recognition performance. For the sake of completeness, we also investigate the recognition performance gains that we can obtain by employing classification ensembles. A ranking system based upon recognition and computational performance is proposed. MCRGNet, Radio Galaxy Zoo, and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.

Structure and Semantics of Tactile Verbs in a Comparative Aspect (On the Material of the English, German and Russian Languages)

2021 ◽  
pp. 58-63
Author(s):  
Yu. A. Sakhno
Keyword(s):  
Comparative Study ◽  
Semantic Features ◽  
Semantic Classification ◽  
Comparative Aspect ◽  
Similarities And Differences ◽  
The Comparative Study ◽  
Linguistic Units

This article deals with the study of the structural and semantic features of tactile verbs (hereinafter TVs) in English, German and Russian. Particular attention is paid to the comparative study of TVs, which allows us to identify structural and semantic similarities and differences of linguistic units studied. The structural and semantic classification of TVs in the compared languages is also provided.

scholarly journals Classification of Tweets based on Emotions using Word Embedding and Random Forest Classifiers

2017 ◽  
Vol 178 (3) ◽  
pp. 1-7
Author(s):  
Parth Vora ◽  
Mansi Khara ◽  
Kavita Kelkar
Keyword(s):  
Random Forest ◽  
Word Embedding

Discriminate Supervised Weighted Scheme for the Classification of Time Series Signals

2021 ◽  
Vol 13 (3) ◽  
pp. 1-16
Author(s):  
Elangovan Ramanujam ◽  
S. Padmavathi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
State Of The Art ◽  
Statistical Significance ◽  
Series Data ◽  
Bag Of Words ◽  
Time Series Classification ◽  
Problem Of Time ◽  
Weighted Matrix

Innovations and applicability of time series data mining techniques have significantly increased the researchers' interest in the problem of time series classification. Several algorithms have been proposed for this purpose categorized under shapelet, interval, motif, and whole series-based techniques. Among this, the bag-of-words technique, an extensive application of the text mining approach, performs well due to its simplicity and effectiveness. To extend the efficiency of the bag-of-words technique, this paper proposes a discriminate supervised weighted scheme to identify the characteristic and representative pattern of a class for efficient classification. This paper uses a modified weighted matrix that discriminates the representative and non-representative pattern which enables the interpretability in classification. Experimentation has been carried out to compare the performance of the proposed technique with state-of-the-art techniques in terms of accuracy and statistical significance.

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