A relational vector space model using an advanced weighting scheme for image retrieval

2011 ◽  
Vol 47 (3) ◽  
pp. 391-414 ◽  
Author(s):  
Jean Martinet ◽  
Yves Chiaramella ◽  
Philippe Mulhem
Keyword(s):  
Image Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Weighting Scheme ◽  
Space Model

scholarly journals Analysis of Text Classification with various Term Weighting Schemes in Vector Space Model

2020 ◽  
Vol 9 (10) ◽  
pp. 390-393
Keyword(s):  
Vector Space ◽  
Text Classification ◽  
Naive Bayes ◽  
Information Gain ◽  
Vector Space Model ◽  
Naïve Bayes ◽  
Weighting Scheme ◽  
Term Weighting ◽  
Space Model ◽  
Weighting Methods

Term Weighting Scheme (TWS) is a key component of the matching mechanism when using the vector space model In the context of information retrieval (IR) from text documents, the this paper described a new approach of term weighting methods to improve the classification performance. In this study, we propose an effective term weighting scheme, which gives highest accuracy with compare to the text classification methods. We compared performance parameter of KNN and Naïve Bayes Classification with different Weighting Method, Weight information gain, SVM and proposed method.We have implemented many term-weighting methods (TWM) on Amazon data collections in combination with Information-Gain and SVM and KNN algorithm and Naïve Bayes Algorithm.

scholarly journals A new vector space model for image retrieval

2017 ◽  
Vol 112 ◽  
pp. 771-779 ◽  
Author(s):  
Hanen Karamti ◽  
Mohamed Tmar ◽  
Faiez Gargouri
Keyword(s):  
Image Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Space Model

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

Aplikasi Rekomendasi Buku Pada Katalog Perpustakaan Universitas Multimedia Nusantara Menggunakan Vector Space Model

2018 ◽  
Vol 9 (2) ◽  
pp. 97-105
Author(s):  
Richard Firdaus Oeyliawan ◽  
Dennis Gunawan
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Vector Model ◽  
Library Management ◽  
Space Model ◽  
Library Management System ◽  
Index Terms ◽  
Library Catalogue ◽  
Language Sample ◽  
F Measure

Library is one of the facilities which provides information, knowledge resource, and acts as an academic helper for readers to get the information. The huge number of books which library has, usually make readers find the books with difficulty. Universitas Multimedia Nusantara uses the Senayan Library Management System (SLiMS) as the library catalogue. SLiMS has many features which help readers, but there is still no recommendation feature to help the readers finding the books which are relevant to the specific book that readers choose. The application has been developed using Vector Space Model to represent the document in vector model. The recommendation in this application is based on the similarity of the books description. Based on the testing phase using one-language sample of the relevant books, the F-Measure value gained is 55% using 0.1 as cosine similarity threshold. The books description and variety of languages affect the F-Measure value gained. Index Terms—Book Recommendation, Porter Stemmer, SLiMS Universitas Multimedia Nusantara, TF-IDF, Vector Space Model

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