Kernel based Learning Suitable for Text Categorization

Jo, Taeho; Lee, M.

doi:10.1109/sera.2007.97

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…Let us survey the previous works on the string vector kernel for modifying the SVM. In 2007, the similarity between string vectors was defined as the string vector kernel, and implemented based on the inverted index of words [4]. In 2007, the string vector kernel was implemented by building the similarity matrix as the alternative way [6].…”

Section: String Vector Based Machine Learning Algorithmsmentioning

confidence: 99%

“…In 2007, the similarity between string vectors was defined as the string vector kernel, and implemented based on the inverted index of words [4]. In 2007, the string vector kernel was implemented by building the similarity matrix as the alternative way [6]. In 2008, the SVM was modified into its string vector based version, and its better performance than the KNN and the Naive Bayes, and the traditional version of SVM, was presented in the text categorization [10].…”

Section: String Vector Based Machine Learning Algorithmsmentioning

confidence: 99%

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Table based K Nearest Neighbor for Text Classification

2024

Preprint

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This article proposes the modified KNN (K Nearest Neighbor)algorithm which receives a table as its input data and is applied tothe text categorization. The motivations of this research are thesuccessful results from applying the table based algorithms to thetext categorizations in previous works and the expectation ofsynergy effect between the text categorization and the wordcategorization. In this research, we define the similarity metricbetween two tables representing texts, modify the KNN algorithm byreplacing the exiting similarity metric by the proposed one, andapply it to the text categorization. The proposed KNN is empiricallyvalidated as the better approach in categorizing texts in newsarticles and opinions. In using the table based KNN algorithm, it iseasier to trace results from categorizing texts.

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Section: String Vector Based Machine Learning Algorithmsmentioning

confidence: 99%

Section: String Vector Based Machine Learning Algorithmsmentioning

confidence: 99%

Table based K Nearest Neighbor for Text Classification

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Let us survey the previous works on the string vector kernel function which indicates the similarity between two string vectors. The string vector kernel was initially defined and implemented based on the inverted index where each word is linked with texts which include itself, in 2007 [5]. The string vector kernel is implemented by defining the similarity matrix as a square matrix which consists of semantic similarities between words, in advance, in 2007 [7].…”

Section: String Vector Based Machine Learning Algorithmsmentioning

confidence: 99%

“…The string vector kernel was initially defined and implemented based on the inverted index where each word is linked with texts which include itself, in 2007 [5]. The string vector kernel is implemented by defining the similarity matrix as a square matrix which consists of semantic similarities between words, in advance, in 2007 [7]. The string vector kernel was used for modifying the SVM into its string vector based version as the approach to the text categorization [11].…”

Section: String Vector Based Machine Learning Algorithmsmentioning

confidence: 99%

Graph Similarity Metric for Modifying K Nearest Neighbor for Classifying Texts

2024

Preprint

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This article proposes the modified KNN (K Nearest Neighbor)algorithm which receives a graph as its input data and is applied tothe text categorization. The graph is more graphical forrepresenting a word and the synergy effect between the textcategorization and the word categorization is expected by combiningthem with each other. In this research, we propose the similaritymetric between two graphs representing words, modify the KNNalgorithm by replacing the exiting similarity metric by the proposedone, and apply it to the text categorization. The proposed KNN isempirically validated as the better approach in categorizing textsin news articles and opinions. In this article, a word is encodedinto a weighted and undirected graph and it is represented into alist of edges.

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Kernel based Learning Suitable for Text Categorization

Cited by 2 publications

References 7 publications

Table based K Nearest Neighbor for Text Classification

Table based K Nearest Neighbor for Text Classification

Graph Similarity Metric for Modifying K Nearest Neighbor for Classifying Texts

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