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Understanding bag-of-words model: a statistical framework
International Journal of Machine Learning and Cybernetics, 2010The bag-of-words model is one of the most popular representation methods for object categorization. The key idea is to quantize each extracted key point into one of visual words, and then represent each image by a histogram of the visual words. For this purpose, a clustering algorithm (e.g., K-means), is generally used for generating the visual words ...
Rong Jin, Zhi-Hua Zhou, Zhou Zhi-Hua
exaly +3 more sources
Graph-based bag-of-words for classification
Pattern Recognition, 2018Abstract This paper introduces the Bag of Graphs (BoG), a Bag-of-Words model that encodes in graphs the local structures of a digital object. We present a formal definition, introducing concepts and rules that make this model flexible and adaptable for different applications. We define two BoG-based methods – Bag of Singleton Graphs (BoSG) and Bag of
Rafael De O Werneck +2 more
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Fuzzy Bag-of-Words Model for Document Representation
IEEE Transactions on Fuzzy Systems, 2018One key issue in text mining and natural language processing is how to effectively represent documents using numerical vectors. One classical model is the Bag-of-Words (BoW). In a BoW-based vector representation of a document, each element denotes the normalized number of occurrence of a basis term in the document.
Rui Zhao, K. Mao
semanticscholar +3 more sources
Explaining a bag of words with hierarchical conceptual labels
World Wide Web, 2020In natural language processing and information retrieval tasks, the bag-of-words model is widely used to represent the semantics of texts. However, it is difficult for machines to sufficiently understand a bag of words as well as the corresponding text without explicit semantic explanation, thus hindering the power of the bag-of-words model in many ...
Haiyun Jiang, Yanghua Xiao, Wei Wang
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Score-based likelihood ratios for linguistic text evidence with a bag-of-words model
Forensic Science International, 2021Shunichi Ishihara
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SLU Journal of Science and Technology, 2022
In Sentiment Analysis, there are three (3) approaches namely, machine learning, lexicon-based and ruled based approaches. This study investigates on machine learning approaches which involves text vectorization or word embedding- an essential step in ...
Haisal Dauda Abubakar, M. Umar
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In Sentiment Analysis, there are three (3) approaches namely, machine learning, lexicon-based and ruled based approaches. This study investigates on machine learning approaches which involves text vectorization or word embedding- an essential step in ...
Haisal Dauda Abubakar, M. Umar
semanticscholar +1 more source
Resume Classification using Elite Bag-of-Words Approach
2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), 2023As technology is advancing day by day, new trends are booming up, like automation, where traditional libraries are being automated to digital libraries.
Muskan Sharma +2 more
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Journal of Psychiatric Research, 2023
Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words.
Dongdong Zhou +9 more
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Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words.
Dongdong Zhou +9 more
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Bag-of-Words Similarity in eXplainable AI
2022eXplainable AI (XAI) does not only lie in the interpretation of the rules generated by AI systems, but also in the evaluation and selection, among many rules automatically generated by large datasets, of those that are more relevant and meaningful for domain experts.
Narteni S +3 more
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A Modified Bag-of-Words Representation for Industrial Alarm Floods*
International Symposium on Advanced Control of Industrial Processes, 2022Alarm floods pose a serious threat to the safety of complex industrial plants by overloading an operator’s cognitive abilities with a large number of alarms in a short period of time.
Haniyeh Seyed Alinezhad +2 more
semanticscholar +1 more source