Relevant bibliographies by topics / Tf-idf vectors

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Tf-idf vectors'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Tf-idf vectors.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Tf-idf vectors"

1

Asgari, Meysam, Jeffrey Kaye, and Hiroko Dodge. "LINGUISTIC MEASURES OF SPOKEN UTTERANCES FOR DETECTING MILD COGNITIVE IMPAIRMENT." Innovation in Aging 3, Supplement_1 (2019): S224—S225. http://dx.doi.org/10.1093/geroni/igz038.826.

Full text
Abstract:
Abstract Studies have shown that speech characteristics can aid in early-identification of those with mild cognitive impairment (MCI). We performed a linguistic analysis on spoken utterances of 41 participants (15 MCI, 26 healthy controls) from conversations with a trained interviewer using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Data came from a randomized controlled behavioral clinical trial (ClinicalTrials.gov: NCT01571427) to examine effects of conversation-based cognitive stimulation on cognitive functions among older adults with normal cognition or MCI, which serve
APA, Harvard, Vancouver, ISO, and other styles
2

Bounabi, Mariem, Karim Elmoutaouakil, and Khalid Satori. "A new neutrosophic TF-IDF term weighting for text mining tasks: text classification use case." International Journal of Web Information Systems 17, no. 3 (2021): 229–49. http://dx.doi.org/10.1108/ijwis-11-2020-0067.

Full text
Abstract:
Purpose This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency (NTF-IDF), is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages. The paper also propose a comparative study between the popular FTF-IDF and NTF-IDF and their impacts on different machine learning (ML) classifiers for document categorization goals. Design/methodology/approach After preprocessing textual dat
APA, Harvard, Vancouver, ISO, and other styles
3

Ni'mah, Ana Tsalitsatun, and Agus Zainal Arifin. "Perbandingan Metode Term Weighting terhadap Hasil Klasifikasi Teks pada Dataset Terjemahan Kitab Hadis." Rekayasa 13, no. 2 (2020): 172–80. http://dx.doi.org/10.21107/rekayasa.v13i2.6412.

Full text
Abstract:
Hadis adalah sumber rujukan agama Islam kedua setelah Al-Qur’an. Teks Hadis saat ini diteliti dalam bidang teknologi untuk dapat ditangkap nilai-nilai yang terkandung di dalamnya secara pegetahuan teknologi. Dengan adanya penelitian terhadap Kitab Hadis, pengambilan informasi dari Hadis tentunya membutuhkan representasi teks ke dalam vektor untuk mengoptimalkan klasifikasi otomatis. Klasifikasi Hadis diperlukan untuk dapat mengelompokkan isi Hadis menjadi beberapa kategori. Ada beberapa kategori dalam Kitab Hadis tertentu yang sama dengan Kitab Hadis lainnya. Ini menunjukkan bahwa ada beberapa
APA, Harvard, Vancouver, ISO, and other styles
4

Grishmanov, E., I. Zakharchenko, P. Berdnik та M. Kasyanenko. "ВИБІР МАТЕМАТИЧНОГО АПАРАТУ ДЛЯ ПОБУДОВИ ВЕКТОРНОЇ МОДЕЛІ ТЕКСТОВИХ ПОВІДОМЛЕНЬ ДЛЯ НАВЧАННЯ ГЛИБОКОЇ НЕЙРОННОЇ МЕРЕЖІ ПРОГНОЗУВАННЮ НЕСПРИЯТЛИВИХ АВІАЦІЙНИХ ПОДІЙ В ПОЛЬОТІ". Системи управління, навігації та зв’язку. Збірник наукових праць 2, № 54 (2019): 18–21. http://dx.doi.org/10.26906/sunz.2019.2.018.

Full text
Abstract:
В роботі проводиться дослідження і вибір математичного апарату для побудови словника і векторної моделі текстових повідомлень для навчання глибокої гібридної нейронної мережі прогнозуванню несприятливих авіаційних подій в польоті. Для визначення вагових значень слів в текстових повідомленнях про несприятливі авіаційнї події в польоті при формуванні словника аналізуються вагові моделі на основі мір TF-IDF, TF-RF і TF-ICF. У якості методів векторного представлення текстової інформації в роботі досліджуються: «мішок слів», латентно-семантичний аналіз (Latent semantic analysis (LSA)), моделі векто
APA, Harvard, Vancouver, ISO, and other styles
5

Mazurek, Marcin, and Mateusz Romaniuk. "Attribution of authorship in instant messaging software applications, based on similarity measures of the stylometric features’ vector." Computer Science and Mathematical Modelling, no. 11-12/2020 (June 30, 2021): 33–41. http://dx.doi.org/10.5604/01.3001.0015.2735.

Full text
Abstract:
This paper describes the issue of authorship attribution based on the content of conversations originating from instant messaging software applications. The results presented in the paper refer to the corpus of conversations conducted in Polish. On the basis of a standardised model of the corpus of conversations, stylometric features were extracted, which were divided into four groups: word and message length distributions, character frequencies, tf-idf matrix and features extracted on the basis of turns (conversational features). The vectors of users’ stylometric features were compared in pai
APA, Harvard, Vancouver, ISO, and other styles
6

Pradhan, Ligaj. "Enhancing Rating Prediction by Discovering and Incorporating Hidden User Associations and Behaviors." International Journal of Multimedia Data Engineering and Management 10, no. 1 (2019): 40–59. http://dx.doi.org/10.4018/ijmdem.2019010103.

Full text
Abstract:
Collaborative filtering (CF)-based rating prediction would greatly benefit by incorporating additional user associations and behavioral similarity. This article focuses on infusing such additional side information in three common techniques used for building CF-based systems. First, multi-view clustering is used over neighborhood-based rating predictions. Secondly, additional user behavior knowledge discovered by mining user reviews are infused into non-negative matrix factorization (NMF) techniques. Finally, the article explores how to infuse such additional behavioral knowledge into a Deep N
APA, Harvard, Vancouver, ISO, and other styles
7

Haq, Bishrul, Ghulam Mujtaba, Zahid Hussain Khand, Javed Ahmad, and Zafar Ali. "A Comparative Study of Sentiment Analysis on Mask-Wearing Practices during the COVID-19 Pandemic." Quaid-e-Awam University Research Journal of Engineering, Science & Technology 18, no. 02 (2020): 116–26. http://dx.doi.org/10.52584/qrj.1802.17.

Full text
Abstract:
COVID-19 has become one of the most highly orated subject matter in these days. Countries have taken many viable actions to prevent the spread of the virus directed by international recommendations, which led to many disputes concerning wearing a face mask as a preventive measure against the virus. This study aims to assess and compare the overall accuracy, macro precision, macro F-measure and macro recall of the different decision models towards the COVID-19 mask-wearing practices via sentiment analysis. Tweets are labeled and text pre-processing techniques are applied as stemming, normalizat
APA, Harvard, Vancouver, ISO, and other styles
8

Xie, Lixia, Ziying Wang, Yue Wang, Hongyu Yang, and Jiyong Zhang. "New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms." Sensors 18, no. 9 (2018): 3047. http://dx.doi.org/10.3390/s18093047.

Full text
Abstract:
This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt when performing multi-keyword ciphertext searches on privacy-protected sensor network cloud platforms. Document vectors are first generated by combining the term frequency-inverse document frequency (TF-IDF) weight factor and the vector space model (VSM). The improved quality hierarchical clustering (IQHC) algorithm then generates document ve
APA, Harvard, Vancouver, ISO, and other styles
9

Ianina, Anastasia, and Konstantin Vorontsov. "Hierarchical Interpretable Topical Embeddings for Exploratory Search and Real-Time Document Tracking." International Journal of Embedded and Real-Time Communication Systems 11, no. 4 (2020): 134–52. http://dx.doi.org/10.4018/ijertcs.2020100107.

Full text
Abstract:
Real-time monitoring of scientific papers and technological news requires fast processing of complicated search demands motivated by thematically relevant information acquisition. For this case, the authors develop an exploratory search engine based on probabilistic hierarchical topic modeling. Topic model gives a low dimensional sparse interpretable vector representation (topical embedding) of a text, which is used for ranking documents by their similarity to the query. They explore several ways of comparing topical vectors including searching with thematically homogeneous text segments. Topi
APA, Harvard, Vancouver, ISO, and other styles
10

Xie, Chunli, Xia Wang, Cheng Qian, and Mengqi Wang. "A Source Code Similarity Based on Siamese Neural Network." Applied Sciences 10, no. 21 (2020): 7519. http://dx.doi.org/10.3390/app10217519.

Full text
Abstract:
Finding similar code snippets is a fundamental task in the field of software engineering. Several approaches have been proposed for this task by using statistical language model which focuses on syntax and structure of codes rather than deep semantic information underlying codes. In this paper, a Siamese Neural Network is proposed that maps codes into continuous space vectors and try to capture their semantic meaning. Firstly, an unsupervised pre-trained method that models code snippets as a weighted series of word vectors. The weights of the series are fitted by the Term Frequency-Inverse Doc
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Tf-idf vectors"

1

Whissell, John. "Significant Feature Clustering." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2926.

Full text
Abstract:
In this thesis, we present a new clustering algorithm we call <em>Significance Feature Clustering</em>, which is designed to cluster text documents. Its central premise is the mapping of raw frequency count vectors to discrete-valued significance vectors which contain values of -1, 0, or 1. These values represent whether a word is <em>significantly negative</em>, <em>neutral</em>, or <em>significantly positive</em>, respectively. Initially, standard tf-idf vectors are computed from raw frequency vectors, then these tf-idf vectors are transformed to significance vectors using a
APA, Harvard, Vancouver, ISO, and other styles
2

Mann, Anna, and Olivia Höft. "Categorization of Swedish e-mails using Supervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296558.

Full text
Abstract:
Society today is becoming more digitalized, and a common way of communication is to send e-mails. Currently, the company Auranest has a filtering method for categorizing e-mails, but the method is a few years old. The filter provides a classification of valuable e-mails for jobseekers, where employers can make contact. The company wants to know if the categorization can be performed with a different method and improved. The degree project aims to investigate whether the categorization can be proceeded with higher accuracy using machine learning. Three supervised machine learning algorithms, Na
APA, Harvard, Vancouver, ISO, and other styles
3

Čeloud, David. "Vyhledávání informací TRECVid Search." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237260.

Full text
Abstract:
The master's thesis deals with Information Retrieval. It summarizes the knowledge in the field of Information Retrieval theory. Furthermore, the work gives an overview of models used in Information Retrieval, the data and the actual issues and their possible solutions. The practical part of the master's thesis is focused on the implementation of methods of information retrieval in textual data. The last part is dedicated to experiments validating the implementation and its possible improvements.
APA, Harvard, Vancouver, ISO, and other styles
4

Novosad, Andrej. "Využití metod dolování dat pro analýzu sociálních sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236424.

Full text
Abstract:
Thesis discusses data mining the social media. It gives an introduction about the topic of data mining and possible mining methods. Thesis also explores social media and social networks, what are they able to offer and what problems do they bring. Three different APIs of three social networking sites are examined with their opportunities they provide for data mining. Techniques of text mining and document classification are explored. An implementation of a web application that mines data from social site Twitter using the algorithm SVM is being described. Implemented application is classifying
APA, Harvard, Vancouver, ISO, and other styles
5

Dočekal, Martin. "Porovnání klasifikačních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403211.

Full text
Abstract:
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Tf-idf vectors"

1

Prabhudesai, Arya. "Generation of Hindi Word Embeddings and Their Utilization in Ranking Documents Using Negative Sampling Architecture, t-SNE Visualization and TF-IDF Based Weighted Average of Vectors." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16681-6_28.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Sidorov, Grigori. "Vector Space Model for Texts and the tf-idf Measure." In Syntactic n-grams in Computational Linguistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14771-6_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Chen, Jindong, Pengjia Yuan, Xiaoji Zhou, and Xijin Tang. "Performance Comparison of TF*IDF, LDA and Paragraph Vector for Document Classification." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2857-1_20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Luz De Araujo, Pedro Henrique, and Teófilo De Campos. "Topic Modelling Brazilian Supreme Court Lawsuits." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200855.

Full text
Abstract:
The present work proposes the use of Latent Dirichlet Allocation to model Extraordinary Appeals received by Brazil’s Supreme Court. The data consist of a corpus of 45,532 lawsuits manually annotated by the Court’s experts with theme labels, a multi-class and multi-label classification task. We initially train models with 10 and 30 topics and analyze their semantics by examining each topic’s most relevant words and their most representative texts, aiming to evaluate model interpretability and quality. We also train models with 30, 100, 300 and 1,000 topics, and quantitatively evaluate their potential using the topics to generate feature vectors for each appeal. These vectors are then used to train a lawsuit theme classifier. We compare traditional bag-of-words approaches (word counts and tf-idf values) with the topic-based text representation to assess topic relevancy. Our topics semantic analysis demonstrate that our models with 10 and 30 topics were capable of capturing some of the legal matters discussed by the Court. In addition, our experiments show that the model with 300 topics was the best text vectoriser and that the interpretable, low dimensional representations it generates achieve good classification results.
APA, Harvard, Vancouver, ISO, and other styles
5

A B, Pawar, Jawale M A, and Kyatanavar D N. "Analyzing Fake News Based on Machine Learning Algorithms." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200146.

Full text
Abstract:
Usages of Natural Language Processing techniques in the field of detection of fake news is analyzed in this research paper. Fake news are misleading concepts spread by invalid resources can provide damages to human-life, society. To carry out this analysis work, dataset obtained from web resource OpenSources.co is used which is mainly part of Signal Media. The document frequency terms as TF-IDF of bi-grams used in correlation with PCFG (Probabilistic Context Free Grammar) on a set of 11,000 documents extracted as news articles. This set tested on classification algorithms namely SVM (Support Vector Machines), Stochastic Gradient Descent, Bounded Decision Trees, Gradient Boosting algorithm with Random Forests. In experimental analysis, found that combination of Stochastic Gradient Descent with TF-IDF of bi-grams gives an accuracy of 77.2% in detecting fake contents, which observes with PCFGs having slight recalling defects
APA, Harvard, Vancouver, ISO, and other styles
6

Bouarara, Hadj Ahmed. "Multi-Agents Machine Learning (MML) System for Plagiarism Detection." In Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3004-6.ch007.

Full text
Abstract:
Day after day the cases of plagiarism increase and become a crucial problem in the modern world caused by the quantity of textual information available in the web. As data mining becomes the foundation for many different domains, one of its chores is a text categorization that can be used in order to resolve the impediment of automatic plagiarism detection. This chapter is devoted to a new approach for combating plagiarism named MML (Multi-agents Machine Learning system) composed of three modules: data preparation and digitalization, using n-gram character or bag of words as methods for the text representation, TF*IDF as weighting to calculate the importance of each term in the corpus in order to transform each document to a vector, and learning and vote phase using three supervised learning algorithms (decision tree c4.5, naïve Bayes, and support vector machine).
APA, Harvard, Vancouver, ISO, and other styles
7

Verma, Vibha, Neha Neha, and Anu G. Aggarwal. "Applications of Machine Learning for Software Management." In Handbook of Research on Emerging Trends and Applications of Machine Learning. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9643-1.ch007.

Full text
Abstract:
Software firms plan all development and management activities strategically to provide the best products and solutions to their user. IT professionals are involved in the process of studying the bugs reported and assign severity to make decisions regarding their resolution. To make the task fast and accurate, developers use automatic methods. Herein, the authors have used feature selection-based classification technique to decide about the severity of reported bugs. TF-IDF feature selection method is used to select the informative terms, determining the severity. Based on selected terms the support vector machine and artificial neural network classifiers are used for classification. A number of performance measures have been used to test the performance of classification. The bug reports of Eclipse project for JDT and platform products were collected from Bugzilla. The results show that classifying bugs on the basis of severity can be effectively improved by feature selection-based strategy.
APA, Harvard, Vancouver, ISO, and other styles
8

Maki, Mohamed Abdulhussain Ali Madan, and Suresh Subramanian. "Using an Artificial Neural Network to Improve Email Security." In Implementing Computational Intelligence Techniques for Security Systems Design. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2418-3.ch006.

Full text
Abstract:
Email is one of the most widely used features of internet, and it is the most convenient method of transferring messages electronically. However, email productivity has been decreased due to phishing attacks, spam emails, and viruses. Recently, filtering the email flow is a challenging task for researchers due to techniques that spammers used to avoid spam detection. This research proposes an email spam filtering system that filters the spam emails using artificial back propagation neural network (BPNN) technique. Enron1 dataset was used, and after the preprocessing, TF-IDF algorithm was used to extract features and convert them into frequency. To select best features, mutual information technique has been applied. Performance of classifiers were measured using BoW, n-gram, and chi-squared methods. BPNN model was compared with Naïve Bayes and support vector machine based on accuracy, precision, recall, and f1-score. The results show that the proposed email spam system achieved 98.6% accuracy with cross-validation.
APA, Harvard, Vancouver, ISO, and other styles
9

Maki, Mohamed Abdulhussain Ali Madan, and Suresh Subramanian. "Using an Artificial Neural Network to Improve Email Security." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch071.

Full text
Abstract:
Email is one of the most widely used features of internet, and it is the most convenient method of transferring messages electronically. However, email productivity has been decreased due to phishing attacks, spam emails, and viruses. Recently, filtering the email flow is a challenging task for researchers due to techniques that spammers used to avoid spam detection. This research proposes an email spam filtering system that filters the spam emails using artificial back propagation neural network (BPNN) technique. Enron1 dataset was used, and after the preprocessing, TF-IDF algorithm was used to extract features and convert them into frequency. To select best features, mutual information technique has been applied. Performance of classifiers were measured using BoW, n-gram, and chi-squared methods. BPNN model was compared with Naïve Bayes and support vector machine based on accuracy, precision, recall, and f1-score. The results show that the proposed email spam system achieved 98.6% accuracy with cross-validation.
APA, Harvard, Vancouver, ISO, and other styles
10

Morra, Emanuele, Roberto Revetria, Danilo Pecorino, Matteo Giudici, and Gabriele Galli. "An Innovative AI-Based System for Corruption Risks Assessment Among Corporate Managers to Support Open Source Analysis." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200564.

Full text
Abstract:
The paper has its focus on the creation of an innovative Natural Language Processing system for the quest of available information and consequent data analysis, aimed at reconstructing the corporate chain and monitoring the sensitive risk of corruption for people involved in command positions. Today, the greatest opportunity in finding information is represented by the Internet or other open sources, where the contents related to corporate managers are continuously posted and updated. Given the vastness of the information dimension, it seems remarkably advantageous to have an intelligent analysis system capable of independently finding, analyzing and synthesizing information related to a set of target subjects. The aim of this document is to describe a forecasting model based on Machine Learning and Artificial Intelligence techniques capable of understanding whether a news item related to an individual (sought during a due diligence process) contains information about crime, investigation, conviction, fraud, corruption or sanction relating to the subject sought. Methods based on Artificial Neural Networks and Support Vector Machine, compared one to the others, are introduced and applied for the scope. In particular, results showed the architecture based on SVM with TF-IDF matrix and test pre-processing outperforms the others discussed in this paper demonstrating high accuracy and precision in prediction new data as well.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Tf-idf vectors"

1

Schofield, Matthew, Gulsum Alicioglu, Russell Binaco, et al. "Convolutional Neural Network for Malware Classification Based on API Call Sequence." In 8th International Conference on Artificial Intelligence and Applications (AIAP 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110106.

Full text
Abstract:
Malicious software is constantly being developed and improved, so detection and classification of malicious applications is an ever-evolving problem. Since traditional malware detection techniques fail to detect new or unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the Windows system API (Application Program Interface) calls. This research uses a database of 5385 instances of API call streams labeled with eight types of malware of the source malicious application
APA, Harvard, Vancouver, ISO, and other styles
2

Danabal, Tharunya, Neethi Sarah John, Abhijeet Pramod Ghawade, and Pranjal Padharinath Ahire. "Cognitive HSE Risk Prediction and Notification Tool Based on Natural Language Processing." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205877-ms.

Full text
Abstract:
Abstract The focus of this work is on developing a cognitive tool that predicts the most frequent HSE hazards with the highest potential severity levels. The tool identifies these risks using a natural language processing algorithm on HSE leading and lagging indicator reports submitted to an oilfield services company’s global HSE reporting system. The purpose of the tool is to prioritize proactive actions and provide focus to raise workforce awareness. A natural language processing algorithm was developed to identify priority HSE risks based on potential severity levels and frequency of occurr
APA, Harvard, Vancouver, ISO, and other styles
3

Park, Jihyun, Junghyun Kim, and Wonyoung Yoo. "TF-IDF based binary fingerprint search with vector quantization error compensation." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354613.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ye-Yi Wang and Alex Acero. "Maximum entropy model parameterization with TF∗IDF weighted vector space model." In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430111.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Fautsch, Claire, and Jacques Savoy. "Adapting the tf idf vector-space model to domain specific information retrieval." In the 2010 ACM Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774454.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Adji, Teguh Bharata, Zainil Abidin, and Hanung Adi Nugroho. "System of negative Indonesian website detection using TF-IDF and Vector Space Model." In 2014 International Conference on Electrical Engineering and Computer Science (ICEECS). IEEE, 2014. http://dx.doi.org/10.1109/iceecs.2014.7045240.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Islam, Md Saiful, Fazla Elahi Md Jubayer, and Syed Ikhtiar Ahmed. "A support vector machine mixed with TF-IDF algorithm to categorize Bengali document." In 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2017. http://dx.doi.org/10.1109/ecace.2017.7912904.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Dadgar, Seyyed Mohammad Hossein, Mohammad Shirzad Araghi, and Morteza Mastery Farahani. "A novel text mining approach based on TF-IDF and Support Vector Machine for news classification." In 2016 IEEE International Conference on Engineering and Technology (ICETECH). IEEE, 2016. http://dx.doi.org/10.1109/icetech.2016.7569223.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Tf-idf vectors' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography